PSK Method for Solving Type-1 and Type-3 Fuzzy Transportation Problems
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This document provides citation information for a chapter titled "PSK Method for Solving Type-1 and Type-3 Fuzzy Transportation Problems" published in the book "Fuzzy Systems: Concepts, Methodologies, Tools, and Applications". It lists the citation in MLA, APA, and Chicago format. The chapter discusses using the PSK method to solve type-1 and type-3 fuzzy transportation problems.
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PSK Method for Solving Type-1 and Type-3 Fuzzy Transportation Problems
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50(3), 247–255. doi:10.1016/0165-0114(92)90223-Q](https://image.slidesharecdn.com/pskmethodforsolvingtype-1andtype-38-180320053558/85/PSK-Method-for-Solving-Type-1-and-Type-3-Fuzzy-Transportation-Problems-22-320.jpg)
PSK Method for Solving Type-1 and Type-3 Fuzzy Transportation Problems
- 1. Cite this Chapter as follows: MLA Kumar, P. Senthil. "PSK Method for Solving Type-1 and Type-3 Fuzzy Transportation Problems." Fuzzy Systems: Concepts, Methodologies, Tools, and Applications. IGI Global, 2017. 367-392. Web. 2 Mar. 2017. doi:10.4018/978-1-5225-1908-9.ch017 APA Kumar, P. S. (2017). PSK Method for Solving Type-1 and Type-3 Fuzzy Transportation Problems. In Fuzzy Systems: Concepts, Methodologies, Tools, and Applications (pp. 367-392). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-1908-9.ch017
- 2. Chicago Kumar, P. Senthil. "PSK Method for Solving Type-1 and Type-3 Fuzzy Transportation Problems." In Fuzzy Systems: Concepts, Methodologies, Tools, and Applications, 367-392 (2017), accessed March 02, 2017. doi:10.4018/978-1-5225-1908-9.ch017
- 3. Fuzzy Systems: Concepts, Methodologies, Tools, and Applications Information Resources Management Association USA
- 4. Editor-in-Chief Mehdi Khosrow-Pour, DBA Information Resources Management Association, USA Associate Editors Steve Clarke, University of Hull, UK Murray E. Jennex, San Diego State University, USA Annie Becker, Florida Institute of Technology, USA Ari-Veikko Anttiroiko, University of Tampere, Finland Editorial Advisory Board Sherif Kamel, American University in Cairo, Egypt In Lee, Western Illinois University, USA Jerzy Kisielnicki, Warsaw University, Poland Amar Gupta, Arizona University, USA Craig van Slyke, University of Central Florida, USA John Wang, Montclair State University, USA Vishanth Weerakkody, Brunel University, UK
- 5. Published in the United States of America by IGI Global Information Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue Hershey PA, USA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: cust@igi-global.com Web site: http://www.igi-global.com Copyright © 2017 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher. For electronic access to this publication, please contact: eresources@igi-global.com. Names: Information Resources Management Association. Title: Fuzzy systems : concepts, methodologies, tools, and applications / Information Resources Management Association, editor. Description: Hershey PA : Information Science Reference, 2017. | Includes bibliographical references. Identifiers: LCCN 2016046977| ISBN 9781522519089 (hardcover) | ISBN 9781522519096 (ebook) Subjects: LCSH: Fuzzy logic. | Fuzzy systems. | Neural networks (Computer science) | Intelligent control systems. Classification: LCC QA9.64 .F8975 2017 | DDC 511.3--dc23 LC record available at https://lccn.loc.gov/2016046977
- 6. Table of Contents Preface................................................................................................................................................... xx Volume I Section 1 Development and Design Methodologies Chapter 1 Uncertain Static and Dynamic Analysis of Imprecisely Defined Structural Systems............................. 1 S. Chakraverty, National Institute of Technology – Rourkela, India Diptiranjan Behera, National Institute of Technology – Rourkela, India Chapter 2 Hierarchical Fuzzy Rule Interpolation and its Application for Hotels Location Selection................... 31 Yanling Jiang, Chongqing University of Science and Technology, China Shangzhu Jin, Chongqing University of Science and Technology, China Jun Peng, Chongqing University of Science and Technology, China Chapter 3 Modified Iterative Methods for Solving Fully Fuzzy Linear Systems................................................... 55 S. A. Edalatpanah, Ayandegan Institute of Higher Education, Tonekabon, Iran Chapter 4 Comparison of Uncertainties in Membership Function of Adaptive Lyapunov NeuroFuzzy-2 for Damping Power Oscillations.................................................................................................................. 74 Laiq Khan, COMSATS Institute of Information Technology, Pakistan Rabiah Badar, COMSATS Institute of Information Technology, Pakistan Saima Ali, COMSATS Institute of Information Technology, Pakistan Umar Farid, COMSATS Institute of Information Technology, Pakistan Chapter 5 Constructing Structural Equation Model Rule-Based Fuzzy System with Genetic Algorithm........... 132 EnDer Su, National Kaohsiung First University of Science and Technology, Taiwan Thomas W. Knowles, Illinois Institute of Technology, USA Yu-Gin Fen, National Kaohsiung First University of Science and Technology, Taiwan
- 7. Chapter 6 A Novel Approach of Restoration of Digital Images Degraded by Impulse Noise............................. 153 Rashmi Kumari, JJTU, India Anupriya Asthana, Galgotias University, India Vikas Kumar, Asia-Pacific Institute of Management, India Chapter 7 Imprecise Knowledge and Fuzzy Modeling in Materials Domain...................................................... 170 Subhas Ganguly, National Institute Technology Raipur, India Shubhabrata Datta, Calcutta Institute of Engineering and Management, India Chapter 8 Assessment of Clinical Decision Support Systems for Predicting Coronary Heart Disease............... 184 Sidahmed Mokeddem, University of Oran 1 Ahmed Ben Bella, Algeria Baghdad Atmani, University of Oran 1 Ahmed Ben Bella, Algeria Chapter 9 Early Warning System Framework Proposal Based on Structured Analytical Techniques, SNA, and Fuzzy Expert System for Different Industries............................................................................... 202 Goran Klepac, University College for Applied Computer Engineering Algebra, Zagreb, Croatia Robert Kopal, University College for Applied Computer Engineering Algebra, Zagreb, Croatia Leo Mrsic, University College for Applied Computer Engineering Algebra, Zagreb, Croatia Chapter 10 Stability Enhancement in Multi-Machine Power Systems by Fuzzy-Based Coordinated AVR-PSS.............................................................................................................................................. 235 Rahmat Khezri, University of Kurdistan, Iran Hassan Bevrani, University of Kurdistan, Iran Chapter 11 Fuzzy Finite Element Method in Diffusion Problems......................................................................... 250 S. Chakraverty, National Institute of Technology – Rourkela, India S. Nayak, National Institute of Technology, India Chapter 12 A Hybrid Model for Rice Disease Diagnosis Using Entropy Based Neuro Genetic Algorithm......... 273 K. Lavanya, VIT University, Vellore, India M.A. Saleem Durai, VIT University, Vellore, India N.Ch.S.N. Iyengar, VIT University, Vellore, India
- 8. Chapter 13 A Fuzzy Model with Thermodynamic Based Consequents and a Niching Swarm-Based Supervisor to Capture the Uncertainties of Damavand Power System................................................ 292 Ahmad Mozaffari, University of Waterloo, Canada Moein Mohammadpour, Babol University of Technology, Iran Alireza Fathi, Babol University of Technology, Iran Mofid Gorji-Bandpy, Babol University of Technology, Iran Chapter 14 Design of a Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) Controller for Position and Angle Control of Inverted Pendulum (IP) Systems............................................................................. 308 Ashwani Kharola, Institute of Technology Management (ITM), India Chapter 15 Intuitionistic Fuzzy Set Theory with Fair Share CPU Scheduler: A Dynamic Approach................... 321 Supriya Raheja, NorthCap University, India Chapter 16 Classification of EEG Signals for Motor Imagery Based on Mutual Information and Adaptive Neuro Fuzzy Inference System............................................................................................................ 347 Shereen A. El-aal, Al-Azhar University, Egypt Rabie A. Ramadan, Cairo University, Egypt Neveen Ghali, Al-Azhar University, Egypt Chapter 17 PSK Method for Solving Type-1 and Type-3 Fuzzy Transportation Problems................................... 367 P. Senthil Kumar, Jamal Mohamed College (Autonomous), India Chapter 18 Fault Detection and Isolation for an Uncertain Takagi-Sugeno Fuzzy System using the Interval Approach.............................................................................................................................................. 393 Hassene Bedoui, University of Monastir, Tunisia Atef Kedher, University of Tunis Manar, Tunisia Kamel Ben Othman, University of Tunis Manar, Tunisia Section 2 Tools and Technologies Chapter 19 Fuzzy Expert System to Diagnose Diabetes Using S Weights for S Fuzzy Assessment Methodology........................................................................................................................................ 418 A. V. Senthil Kumar, Hindusthan College of Arts and Science, India M. Kalpana, Tamil Nadu Agricultural University, India
- 9. Chapter 20 Hybrid Fuzzy Neural Search Retrieval System................................................................................... 443 Rawan Ghnemat, Princess Sumaya University for Technology, Jordan Adnan Shaout, The University of Michigan – Dearborn, USA Chapter 21 A Multi-Objective Fuzzy Ant Colony Optimization Algorithm for Virtual Machine Placement....... 459 Boominathan Perumal, VIT University, Vellore, India Aramudhan M., Perunthalaivar Kamarajar Institute of Engineering and Technology, India Chapter 22 Fuzzy Adaptive Controller for Uncertain Multivariable Nonlinear Systems with Both Sector Nonlinearities and Dead-Zones........................................................................................................... 487 Abdesselem Boulkroune, University of Jijel, Algeria Chapter 23 A Two-Level Fuzzy Value-Based Replica Replacement Algorithm in Data Grids............................. 516 Nazanin Saadat, Science and Research Branch, Islamic Azad University, Iran Amir Masoud Rahmani, Science and Research Branch, Islamic Azad University, Iran Chapter 24 A Fuzzy Expert System for Star Classification Based on Photometry................................................ 540 Aida Pakniyat, Kharazmi University, Iran Rahil Hosseini, Shahr-e-Qods Branch, Islamic Azad University, Iran Mahdi Mazinai, Shahr-e-Qods Branch, Islamic Azad University, Iran Chapter 25 Personalized Neuro-Fuzzy Expert System for Determination of Nutrient Requirements................... 551 Priti Srinivas Sajja, Sardar Patel University, India Jeegar Ashokkumar Trivedi, Sardar Patel University, India Volume II Chapter 26 Movie Recommendation System Based on Fuzzy Inference System and Adaptive Neuro Fuzzy Inference System.................................................................................................................................. 573 Mahfuzur Rahman Siddiquee, North South University, Bangladesh Naimul Haider, North South University, Bangladesh Rashedur M. Rahman, North South University, Bangladesh Chapter 27 Fuzzy Logic-Based Cluster Heads Percentage Calculation for Improving the Performance of the LEACH Protocol.................................................................................................................................. 609 Omar Banimelhem, Jordan University of Science and Technology, Jordan Eyad Taqieddin, Jordan University of Science and Technology, Jordan Moad Y. Mowafi, Jordan University of Science and Technology, Jordan Fahed Awad, Jordan University of Science and Technology, Jordan Feda’ Al-Ma’aqbeh, Jordan University of Science and Technology, Jordan
- 10. Chapter 28 Evaluation of Human Machine Interface (HMI) on a Digital and Analog Control Room in Nuclear Power Plants Using a Fuzzy Logic Approach...................................................................................... 628 Pola Lydia Lagari, Purdue University, USA Antonia Nasiakou, Purdue University, USA Miltiadis Alamaniotis, Purdue University, USA Chapter 29 Including Client Opinion and Employee Engagement in the Strategic Human Resource Management: An Advanced SWOT- FUZZY Decision Making Tool................................................ 647 Rachid Belhaj, Mohammed V University, Morocco Mohamed Tkiouat, Mohammed V University, Morocco Chapter 30 An Optimal Fuzzy Load Balanced Adaptive Gateway Discovery for Ubiquitous Internet Access in MANET............................................................................................................................................... 663 Prakash Srivastava, Madan Mohan Malaviya University of Technology, India Rakesh Kumar, Madan Mohan Malaviya University of Technology, India Chapter 31 A Hybrid System Based on FMM and MLP to Diagnose Heart Disease............................................ 682 Swati Aggarwal, NSIT, India Venu Azad, Government Girls PG College, India Chapter 32 Strictness Petroleum Prediction System Based on Fuzzy Model........................................................ 715 Senan A. Ghallab, Ain Shams University, Egypt Nagwa. L. Badr, Ain Shams University, Egypt Abdel Badeeh Salem, Ain Shams University, Egypt M. F. Tolba, Ain Shams University, Egypt Chapter 33 Fuzzy-Based Matrix Converter Drive for Induction Motor................................................................. 738 Chitra Venugopal, University of KwaZulu-Natal, South Africa Chapter 34 A Fuzzy-Based Calorie Burn Calculator for a Gamified Walking Activity Using Treadmill............. 763 Prabhakar Rontala Subramaniam, University of KwaZulu-Natal, South Africa Chitra Venugopal, University of KwaZulu-Natal, South Africa Arun Kumar Sangaiah, VIT University, India Chapter 35 Application of Fuzzy Logic for Mapping the Agro-Ecological Zones................................................ 782 Bistok Hasiholan Simanjuntak, Satya Wacana Christian University, Indonesia Sri Yulianto Joko Prasetyo, Satya Wacana Christian University, Indonesia Kristoko Dwi Hartomo, Satya Wacana Christian University, Indonesia Hindriyanto Dwi Purnomo, Satya Wacana Christian University, Indonesia
- 11. Chapter 36 Prediction of Solar and Wind Energies by Fuzzy Logic Control......................................................... 807 Sanaa Faquir, University Sidi Mohamed Ben Abdallah, Morocco Ali Yahyaouy, University Sidi Mohamed Ben Abdallah, Morocco Hamid Tairi, University Sidi Mohamed Ben Abdallah, Morocco Jalal Sabor, Ecole Nationale Superieure d’Arts et Metiers (ENSAM), Morocco Chapter 37 Enhancement of Turbo-Generators Phase Backup Protection Using Adaptive Neuro Fuzzy Inference System.................................................................................................................................. 835 Mohamed Salah El-Din Ahmed Abdel Aziz, Dar Al-Handasah (Shair and partners), Egypt Mohamed Elsamahy, The Higher Institute of Engineering, El-Shorouk Academy, Egypt Mohamed A. Moustafa Hassan, Cairo University, Egypt Fahmy M. A. Bendary, Benha University, Egypt Chapter 38 Fuzzy Rule Based Environment Monitoring System for Weather Controlled Laboratories Using Arduino................................................................................................................................................ 855 S. Sasirekha, SSN College of Engineering, India S. Swamynathan, Anna University, India Chapter 39 Fuzzy Labeled Transition Refinement Tree: Application to Stepwise Designing Multi Agent Systems................................................................................................................................................ 873 Sofia Kouah, University of Constantine 2, Algeria University of Oum El Bouaghi, Algeria Djamel-Eddine Saidouni, University of Constantine 2, Algeria Chapter 40 Rule-Based Systems for Medical Diagnosis........................................................................................ 906 V. S. Giridhar Akula, Methodist College of Engineering and Technology, India Section 3 Utilization and Application Chapter 41 Implementation of Fuzzy Technology in Complicated Medical Diagnostics and Further Decision............................................................................................................................................... 935 A. B. Bhattacharya, University of Kalyani, India Arkajit Bhattacharya, M. G. M. Medical College and Hospital, India Chapter 42 Intelligent Decision Making and Risk Analysis of B2c E-Commerce Customer Satisfaction............ 969 Masoud Mohammadian, University of Canberra, Australia
- 12. Chapter 43 Fuzzy Logic Based Approach for Power System Fault Section Analysis............................................ 987 Neeti Dugaya, Sagar Institute of Research, Technology and Science, India Smita Shandilya, Sagar Institute of Research, Technology and Science, India Chapter 44 Some Recent Defuzzification Methods.............................................................................................. 1003 Harendra Kumar, Gurukula Kangari University, India Chapter 45 Application of Fuzzy Expert System in Medical Treatment............................................................. 1020 Kajal Ghosal, Chronic Disease and Oncological Homeopathic Consultant, India Partha Haldar, Jadavpur University, India Goutam Sutradhar, Jadavpur University, India Chapter 46 Predicting Uncertain Behavior and Performance Analysis of the Pulping System in a Paper Industry Using PSO and Fuzzy Methodology................................................................................... 1070 Harish Garg, Indian Institute of Technology-Roorkee, India Monica Rani, Indian Institute of Technology-Roorkee, India S.P. Sharma, Indian Institute of Technology-Roorkee, India Chapter 47 Vague Correlation Coefficient of Interval Vague Sets and its Applications to Topsis in MADM Problems............................................................................................................................................ 1110 John Robinson P., Bishop Heber College (Autonomous), India Henry Amirtharaj E. C., Bishop Heber College (Autonomous), India Volume III Chapter 48 A Fuzzy-Based Approach to Support Decision Making in Complex Military Environments.......... 1150 Timothy P. Hanratty, US Army Research Laboratory, Aberdeen Proving Ground, USA E. Allison Newcomb, Towson University, USA Robert J. Hammell II, Towson University, USA John T. Richardson, US Army Research Laboratory, Aberdeen Proving Ground, USA Mark R. Mittrick, US Army Research Laboratory, Aberdeen Proving Ground, USA Chapter 49 Nonlinear System Identification of Smart Buildings......................................................................... 1183 Soroush Mohammadzadeh, University of Oklahoma, USA Yeesock Kim, Worcester Polytechnic Institute (WPI), USA
- 13. Chapter 50 Fuzzy Logic-Based Intelligent Control System for Active Ankle Foot Orthosis.............................. 1203 M. Kanthi, Manipal University, India Chapter 51 Knowledge Representation Using Fuzzy XML Rules in Web-Based Expert System for Medical Diagnosis........................................................................................................................................... 1237 Priti Srinivas Sajja, Sardar Patel University, India Chapter 52 Improvement of JXTA-Overlay P2P Platform: Evaluation for Medical Application and Reliability........................................................................................................................................... 1268 Yi Liu, Fukuoka Institute of Technology (FIT), Japan Shinji Sakamoto, Fukuoka Institute of Technology (FIT), Japan Keita Matsuo, Fukuoka Prefectural Fukuoka Technical High School, Japan Makoto Ikeda, Fukuoka Institute of Technology (FIT), Japan Leonard Barolli, Fukuoka Institute of Technology (FIT), Japan Fatos Xhafa, Technical University of Catalonia, Spain Chapter 53 Bio-Inspired Computing through Artificial Neural Network............................................................. 1285 Nilamadhab Dash, C. V. Raman College of Engineering, India Rojalina Priyadarshini, C. V. Raman College of Engineering, India Brojo Kishore Mishra, C. V. Raman College of Engineering, India Rachita Misra, C. V. Raman College of Engineering, India Chapter 54 Trust Calculation Using Fuzzy Logic in Cloud Computing.............................................................. 1314 Rajanpreet Kaur Chahal, Panjab University, India Sarbjeet Singh, Panjab University, India Chapter 55 Fuzzy Decision Support System for Coronary Artery Disease Diagnosis Based on Rough Set Theory................................................................................................................................................ 1367 Noor Akhmad Setiawan, Universitas Gadjah Mada, Indonesia Chapter 56 Artificial Intelligent Approaches for Prediction of Longitudinal Wave Velocity in Rocks............... 1385 A. K. Verma, Indian School of Mines, India T. N. Singh, Indian Institute of Technology, India Sachin Maheshwar, Indian School of Mines, India
- 14. Chapter 57 An Adaptive Path Planning Based on Improved Fuzzy Neural Network for Multi-Robot Systems.............................................................................................................................................. 1396 Zhiguo Shi, University of Science and Technology, China Huan Zhang, University of Science and Technology, China Jingyun Zhou, University of Science and Technology, China Junming Wei, Australian National University, Australia Chapter 58 Information Systems on Hesitant Fuzzy Sets.................................................................................... 1425 Deepak D., National Institute of Technology Calicut, India Sunil Jacob John, National Institute of Technology Calicut, India Chapter 59 Artificial Intelligence Methods and Their Applications in Civil Engineering.................................. 1453 Gonzalo Martínez-Barrera, Universidad Autónoma del Estado de México, Mexico Osman Gencel, Bartin University, Turkey Ahmet Beycioglu, Düzce University, Turkey Serkan Subaşı, Düzce University, Turkey Nelly González-Rivas, Joint Center for Research in Sustainable Chemistry (CCIQS), Mexico Chapter 60 Contrasting Correlation Coefficient with Distance Measure in Interval Valued Intuitionistic Trapezoidal Fuzzy MAGDM Problems............................................................................................. 1478 John P. Robinson, Bishop Heber College, India Chapter 61 A Study on Hybridization of Intelligent Techniques in Bioinformatics............................................ 1518 Peyakunta Bhargavi, Sri Padmavati Mahila University, India S. Jyothi, Sri Padmavati Mahila University, India D. M. Mamatha, Sri Padmavati Mahila Univeristy, India Chapter 62 Dynamic Behaviour and Crack Detection of a Multi Cracked Rotating Shaft using Adaptive Neuro-Fuzzy-Inference System: Vibration Analysis of Multi Cracked Rotating Shaft..................... 1540 Rajeev Ranjan, Haldia Institute of Technology, India Section 4 Organizational and Social Implications Chapter 63 Modeling Conflict Dynamics: System Dynamic Approach............................................................... 1553 Janez Usenik, University of Maribor, Slovenia Tit Turnsek, Landscape Governance College Grm, Slovenia
- 15. Chapter 64 Fuzzy Opinion: Detection of Opinion Based on SentiWordNet Dictionary by Using Fuzzy Logic.................................................................................................................................................. 1576 Mohamed Amine Boudia, Dr. Tahar Moulay University of Saida, Algeria Reda Mohamed Hamou, Dr. Tahar Moulay University of Saida, Algeria Abdelmalek Amine, Dr. Tahar Moulay University of Saida, Algeria Chapter 65 Adjust Fuzzy Model Parameters for Head Election in Wireless Sensor Network Protocols............. 1596 Walaa Abd el Aal Afifi, ISSR-Cairo University, Egypt Hesham Ahmed Hefny, ISSR-Cairo University, Egypt Chapter 66 Bidder Selection in Public Procurement using a Fuzzy Decision Support System........................... 1620 Vjekoslav Bobar, University of Belgrade, Serbia Ksenija Mandic, University of Belgrade, Serbia Milija Suknovic, University of Belgrade, Serbia Chapter 67 Fuzzy Dynamic Load Balancing in Virtualized Data Centers of SaaS Cloud Provider.................... 1643 Md. S. Q. Zulkar Nine, North South University, Bangladesh Abul Kalam Azad, North South University, Bangladesh Saad Abdullah, North South University, Bangladesh Rashedur M. Rahman, North South University, Bangladesh Section 5 Emerging Trends Chapter 68 Emerging Application of Fuzzy Expert System in Medical Domain................................................ 1667 A. V. Senthil Kumar, Hindusthan College of Arts and Science, India M. Kalpana, Tamil Nadu Agricultural University, India Chapter 69 Fuzzy Critical Path Method Based on a New Approach of Ranking Fuzzy Numbers Using Centroid of Centroids......................................................................................................................... 1690 N. Ravi Shankar, GITAM University, India B. Pardha Saradhi, Dr. L.B. College, India S. Suresh Babu, GITAM University, India Chapter 70 MAGDM-Miner: A New Algorithm for Mining Trapezoidal Intuitionistic Fuzzy Correlation Rules.................................................................................................................................................. 1708 John P. Robinson, Bishop Heber College, India Henry Amirtharaj, Bishop Heber College, India
- 16. Chapter 71 Advances in QoS/E Characterization and Prediction for Next Generation Mobile Communication Systems.............................................................................................................................................. 1739 Charalampos N. Pitas, National Technical University of Athens, Greece Apostolos G. Fertis, SMA und Partner AG, Zurich, Switzerland Dimitris E. Charilas, National Technical University of Athens, Greece Athanasios D. Panagopoulos, National Technical University of Athens, Greece Index....................................................................................................................................................xxii
- 17. 367 Copyright © 2017, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 17 DOI: 10.4018/978-1-5225-1908-9.ch017 ABSTRACT In conventional transportation problem (TP), supplies, demands and costs are always certain. In this paper, the author tried to categories the TP under the mixture of certain and uncertain environment and formulates the problem and utilizes the crisp numbers, triangular fuzzy numbers (TFNs) and trapezoidal fuzzy numbers (TrFNs) to solve the TP. The existing ranking procedure of Liou and Wang is used to transform the type-1 and type-3 fuzzy transportation problem (FTP) into a crisp one so that the con- ventional method may be applied to solve the TP. The solution procedure differs from TP to type-1 and type-3 FTP in allocation step only. Therefore, the new method called PSK method and new multiplication operation on TrFN is proposed to find the mixed optimal solution in terms of crisp numbers, TFNs and TrFNs. The main advantage of this method is computationally very simple, easy to understand and also the optimum objective value obtained by our method is physically meaningful. The effectiveness of the proposed method is illustrated by means of a numerical example. 1. INTRODUCTION The transportation problem is a special class of linear programming problem which deals with the dis- tribution of single homogeneous product from various origins(sources) to various destinations(sinks). The objective of the transportation problem is to determine the optimal amount of a commodity to be transported from various supply points to various demand points so that the total transportation cost is minimum for a minimization problem or total transportation profit is maximum for a maximization problem. The unit costs, that is, the cost of transporting one unit from a particular supply point to a particular demand point, the amounts available at the supply points and the amounts required at the demand points PSK Method for Solving Type-1 and Type-3 Fuzzy Transportation Problems P. Senthil Kumar Jamal Mohamed College (Autonomous), India
- 18. 368 PSK Method for Solving Type-1 and Type-3 Fuzzy Transportation Problems are the parameters of the transportation problem. Efficient algorithms have been developed for solving transportationproblemswhenthecostcoefficients,thedemandandsupplyquantitiesareknownprecisely. In the history of mathematics, Hitchcock (1941) originally developed the basic transportation prob- lem. Charnes and Cooper (1954) developed the stepping stone method which provides an alternative way of determining the simplex method information. Appa (1973) discussed several variations of the transportation problem. Arsham et al. (1989) proposed a simplex type algorithm for general transporta- tion problems. An Introduction to Operations Research Taha (2008) deals the transportation problem. In today’s real world problems such as in corporate or in industry many of the distribution problems are imprecise in nature due to variations in the parameters. To deal quantitatively with imprecise infor- mation in making decision, Zadeh (1965) introduced the fuzzy set theory and has applied it successfully in various fields. The use of fuzzy set theory becomes very rapid in the field of optimization after the pioneering work done by Bellman and Zadeh (1970). The fuzzy set deals with the degree of membership (belongingness) of an element in the set. In a fuzzy set the membership value (level of acceptance or level of satisfaction) lies between 0 and 1 where as in crisp set the element belongs to the set represent 1 and the element not belongs to the set represent 0. Due to the applications of fuzzy set theory, several authors like Oheigeartaigh (1982) presented an algorithm for solving transportation problems where the availabilities and requirements are fuzzy sets with linear or triangular membership functions. Chanas et al. (1984) presented a fuzzy linear program- ming model for solving transportation problems with fuzzy supply, fuzzy demand and crisp costs. Chanas et al. (1993) formulated the fuzzy transportation problems in three different situations and proposed method for solving the formulated fuzzy transportation problems. Chanas and Kuchta (1996) proposed the concept of the optimal solution for the transportation problem with fuzzy coefficients expressed as fuzzy numbers, and developed an algorithm for obtaining the optimal solution. Chanas and Kuchta (1998) developed a new method for solving fuzzy integer transportation problem by representing the supply and demand parameters as L-R type fuzzy numbers. Saad and Abbas (2003) proposed an algorithm for solving the transportation problems under fuzzy environment. Liu and Kao (2004) presented a method for solving fuzzy transportation problems based on extension principle. Chiang (2005) proposed a method to find the optimal solution of transportation problems with fuzzy requirements and fuzzy availabilities. Gani and Razak (2006) obtained a fuzzy solution for a two stage cost minimizing fuzzy transportation problem in which availabilities and requirements are trapezoidal fuzzy numbers using a parametric approach. Das and Baruah (2007) discussed Vogel’s approximation method to find the fuzzy initial basic feasible solution of fuzzy transportation problem in which all the parameters (supply, demand and cost) are represented by triangular fuzzy numbers. Li et al. (2008) pro- posed a new method based on goal programming approach for solving fuzzy transportation problems with fuzzy costs. Chen et al. (2008) proposed the methods for solving transportation problems on a fuzzy network. Lin (2009) used genetic algorithm for solving transportation problems with fuzzy coefficients. Dinagar and Palanivel (2009) investigated the transportation problem in fuzzy environment using trapezoidal fuzzy numbers. De and Yadav (2010) modified the existing method (Kikuchi 2000) by using trapezoidal fuzzy numbers instead of triangular fuzzy numbers. Pandian et al. (2010) proposed a new algorithm for find- ing a fuzzy optimal solution for fuzzy transportation problem where all the parameters are trapezoidal fuzzy numbers. Mohideen and Kumar (2010) did a comparative study on transportation problem in fuzzy environment. Sudhakar et al. (2011) proposed a different approach for solving two stage fuzzy transpor- tation problems in which supplies and demands are trapezoidal fuzzy numbers. Hadi Basirzadeh (2011)
- 19. 24 more pages are available in the full version of this document, which may be purchased using the Add to Cart button on the product's webpage: www.igi-global.com/chapter/psk-method-for-solving-type-1-and-type-3-fuzzy- transportation-problems/178403?camid=4v1 This title is available in InfoSci-Books, InfoSci-Computer Science and Information Technology, Science, Engineering, and Information Technology, InfoSci-Media and Communication Science and Technology, Communications, Social Science, and Healthcare. Recommend this product to your librarian: www.igi-global.com/e-resources/library-recommendation/?id=1 Related Content Mapping Mobile Statechart Diagrams to the -Calculus using Graph Transformation: An Approach for Modeling, Simulation and Verification of Mobile Agent-based Software Systems Aissam Belghiat and Allaoua Chaoui (2016). International Journal of Intelligent Information Technologies (pp. 1-20). www.igi-global.com/article/mapping-mobile-statechart-diagrams-to-the--calculus-using-graph- transformation/171438?camid=4v1a Application of Fuzzy Expert System in Medical Treatment Kajal Ghosal, Partha Haldar and Goutam Sutradhar (2017). Fuzzy Systems: Concepts, Methodologies, Tools, and Applications (pp. 1020-1069). www.igi-global.com/chapter/application-of-fuzzy-expert-system-in-medical- treatment/178433?camid=4v1a Robust Algorithms for DOA Estimation and Adaptive Beamforming in Wireless Mobile Communications R. M. Shubair, K. O. AlMidfa, A. Al-Marri and M. Al-Nuaimi (2008). Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications (pp. 1036-1047). www.igi-global.com/chapter/robust-algorithms-doa-estimation-adaptive/24329?camid=4v1a Telehomecare in The Netherlands: Barriers to Implementation H.S.M. Kort and J. van Hoof (2012). International Journal of Ambient Computing and Intelligence (pp. 64- 73). www.igi-global.com/article/telehomecare-netherlands-barriers- implementation/66860?camid=4v1a
- 20. 389 PSK Method for Solving Type-1 and Type-3 Fuzzy Transportation Problems Values of µ Z c( ) at different values of c can be determined using equations given below: µ Z c for c c for c for c( ) = ≤ − ≤ ≤ ≤ ≤ − 0 104 104 64 104 168 1 168 184 248 , , , cc for c for c 64 184 248 0 248 , , ≤ ≤ ≥ By using the proposed method a decision maker has the following advantages: 1. The proposed method gives the optimal solution in terms of mixed fuzzy numbers. Moreover, the proposed method gives the opportunity to the decision maker to solve all the types of FTP; 2. The proposed method is computationally very simple and easy to understand. 7. CONCLUSION On the basis of the present study, it can be concluded that the type-1, type-2 and type-4 FTP which can be solved by the existing methods (Pandian and Natarajan (2010), Dinagar and Palanivel (2009), Rani, Gulathi, and Kumar (2014), Hadi Basirzadeh (2011), Gani and Razak (2006)) can also be solved by the proposed method. However, it is much easier to apply the proposed method as compared to all the Figure 1. Graphical representation of type-3 fuzzy transportation cost
- 21. 390 PSK Method for Solving Type-1 and Type-3 Fuzzy Transportation Problems existing methods. Also, new method and new multiplication operation on TrFN is proposed to compute the optimal objective values in terms of trapezoidal fuzzy number which are very simple and easy to understand and it can be easily applied by decision maker to solve type-1 and type-3 FTP. The proposed method gives the optimal solution in terms of mixed fuzzy numbers. Hence the proposed method gives the opportunity to the decision maker to solve all the types of FTP and computationally very simple when compared to all the existing methods. REFERENCES Appa, G. M. (1973). The transportation problem and its variants. The Journal of the Operational Re- search Society, 24(1), 79–99. doi:10.1057/jors.1973.10 Arsham, H., Kahn, A. B. (1989). A simplex-type algorithm for general transportation problems: An alternative to stepping-stone. The Journal of the Operational Research Society, 40(6), 581–590. doi:10.1057/jors.1989.95 Basirzadeh, H. (2011). An approach for solving fuzzy transportation problem. Applied Mathematical Sciences, 5(32), 1549-1566. Bellman, R. E., Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management science, 17,: B 141 – B 164. Biswas, A., Modak, N. (2012). Using Fuzzy Goal Programming Technique to Solve Multiobjective Chance Constrained Programming Problems in a Fuzzy Environment. International Journal of Fuzzy System Applications, 2(1), 71–80. doi:10.4018/IJFSA.2012010105 Chanas, S., Delgado, M., Verdegay, J. L., Vila, M. A. (1993). Interval and fuzzy extensions of classical transportation problems. Transportation Planning and Technology, 17(2), 203–218. doi:10.1080/03081069308717511 Chanas, S., Kołodziejczyk, W., Machaj, A. (1984). A fuzzy approach to the transportation problem. Fuzzy Sets and Systems, 13(3), 211–221. doi:10.1016/0165-0114(84)90057-5 Chanas, S., Kuchta, D. (1996). A concept of the optimal solution of the transportation problem with fuzzy cost coefficients. Fuzzy Sets and Systems, 82(3), 299–305. doi:10.1016/0165-0114(95)00278-2 Chanas, S., Kuchta, D. (1998). Fuzzy integer transportation problem. Fuzzy Sets and Systems, 98(3), 291–298. doi:10.1016/S0165-0114(96)00380-6 Charnes, A., Cooper, W. W. (1954). The stepping stone method of explaining linear programming calculations in transportation problems. Management Science, 1(1), 49–69. doi:10.1287/mnsc.1.1.49 Chen, M., Ishii, H., Wu, C. (2008). Transportation problems on a fuzzy network. International Journal of Innovative Computing, Information, Control, 4(5), 1105–1109. Chen, S. H., Hsieh, C. H. (1999). Graded mean integration representation of generalized fuzzy num- bers. Journal of Chinese Fuzzy System, 5, 1-7.
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- 24. Index A A* 370, 1396-1397, 1399, 1409, 1415, 1420, 1424 Action Refinement 873-874, 883, 887, 902-903 Active AFO 1203, 1205-1206, 1213-1215, 1222, 1231, 1236 Actuator Nonlinearities 487-488, 507, 515 Adaptive Control 75, 115, 456, 488, 501, 509, 511, 515, 1203, 1396-1397, 1420 Adaptive Gateway Discovery 663, 665-666, 678 AdaptiveNeuro-FuzzyInferenceSystem(ANFIS)153- 154, 157, 177, 293, 308, 347, 354, 420, 443-445, 449-450, 456, 573, 594, 835, 837-838, 847, 950, 1183-1184, 1186, 1192, 1197, 1368, 1385, 1390, 1393, 1457, 1542, 1545, 1678 Advisory System 554, 571 AFRAS 1327 Aggregation 298, 420, 430-431, 442, 449, 464, 523, 611, 825, 920, 923-924, 928, 1045-1046, 1049- 1051, 1177, 1328, 1340-1341, 1360, 1426, 1457, 1478-1482, 1530, 1564, 1566, 1630-1631, 1637, 1667, 1677, 1708, 1710-1712, 1718 Agro-Ecological Zone 782-784, 792-793, 801, 806 Analog 628, 630, 633, 639-643, 860, 938-939, 1220 Ant Colony Optimization (ACO) 459-461, 464, 468, 479, 666, 1291 Ant Q System 462, 466 Ant System 461-462, 465-467, 469, 475, 477 Antecedent 32, 36-38, 79, 83-84, 99-100, 241, 285, 296, 298-300, 302, 304, 418, 422, 428, 430-431, 442,461,472,521,611,907-908,923,927,1021, 1043-1044, 1046-1048, 1156, 1158-1159, 1218, 1224, 1226, 1229, 1246, 1248, 1274, 1319, 1322, 1333, 1390, 1407, 1461, 1564-1566, 1601, 1618- 1619, 1671-1672, 1674, 1677, 1688 Arduino 855, 860-861, 863-864, 872 Artificial Earthquake 1183, 1189, 1195-1198 Artificialintelligence(AI)79,130,187,552,684,715, 749, 935, 938, 943, 957, 988-989, 1215, 1217, 1285, 1288, 1367, 1380, 1386, 1388, 1397, 1425, 1450, 1453-1454, 1466, 1470, 1477, 1521, 1525, 1577, 1667-1669, 1673, 1688, 1764 ArtificialNeuralNetworks(ANN)79-80,86,154-155, 171, 180, 218-219, 274, 276, 279, 301, 419-420, 444, 449, 541, 553-554, 557-558, 560-562, 567, 569, 571-572, 682-683, 685-690, 705, 717, 758, 838, 1020, 1074, 1184, 1197, 1204, 1285-1286, 1291-1292, 1294-1295, 1299, 1309, 1367, 1386- 1388, 1398, 1404, 1453, 1477, 1519, 1679, 1759 AVR 235-239, 241-242, 244-246, 860 B Back Propagation 87, 155, 187, 273-274, 278, 284, 286, 289, 314, 559, 569, 571, 684, 687, 691, 707, 758, 950, 1298-1299, 1309, 1389-1390, 1527, 1542, 1752 Back-Propagation Algorithm (BPA) 74, 594, 1186, 1304, 1527 Backward Chaining 541, 545, 557, 906-907, 913, 917, 919-920, 922, 924, 928, 932, 1245, 1267 Belief Rule Base Systems 932 Betweenness Centrality 233 Bidder Selection 1620-1623, 1628, 1631, 1635, 1637 Bioinformatics 517, 1291, 1518-1522, 1524-1528, 1534-1535, 1537-1538 Bisimulation Relation 888, 890 BMR 766 Brain Computer Interface (BCI) 347 C Calorie 763-771, 774, 776-779, 781 Calorie burn 763, 765-771, 774, 776-779 CDSS 184-188, 192, 194, 197, 274, 685, 709, 920- 921, 923-925 cEnter simulator 990, 1001 Certainty Factor 420-421, 423-424, 442, 1678 Volume I pp. 1-572; Volume II pp. 573-1149; Volume III 1150-1765 xxii
- 25. Index circuit breakers 987-997 CivilEngineering1184-1185,1453-1454,1470-1471, 1477 Classical Approach 967, 1587, 1592-1593 Client Opinion 647 Cloud Computing 459-460, 1314-1315, 1341, 1351, 1362-1364, 1366, 1643 Cluster Head 610, 612-613, 1596-1598, 1603-1605, 1611, 1615, 1618 Clustering Algorithms 624, 839, 1524, 1599, 1601, 1606, 1610-1611, 1614, 1618 coefficient matrix 6, 10, 57-58, 61, 252, 256 Collaborative Filtering 574-576 Computational Intelligence 171, 274, 612, 721, 1072, 1285, 1369, 1520, 1756 Computer Algorithm 807, 820 Concrete Technology 1453-1454, 1462, 1470-1471, 1477 Coronary Artery Disease (CAD) 184-185, 685, 1367- 1369 Correlation coefficient 275-276, 574, 578, 580-582, 596, 606, 717, 1110-1113, 1115-1116, 1118- 1119, 1122-1123, 1127-1130, 1132, 1134, 1136, 1145-1147, 1387, 1478-1480, 1501-1503, 1510- 1511, 1513, 1711-1713, 1717-1718, 1721, 1725, 1727-1730, 1733 CPU Scheduler 321-322, 324-325, 327 Crisp Values 376, 431, 442, 556, 565, 571, 615, 633, 686, 723-724, 793, 829, 856, 1003-1004, 1017, 1094, 1111, 1245, 1255, 1262, 1331, 1458, 1462, 1633, 1649, 1667, 1674 Critical Path Method 369, 1690-1691, 1693-1695, 1698, 1701, 1703 Currency Volatility 132 Cyber Intelligence 1150-1151, 1174, 1177-1179 Cycloconverter 740-741, 762 D Data Acquisition 292, 821, 826, 988-989, 1001, 1203, 1220-1221, 1226, 1230-1231, 1544-1545, 1753 Data Availability 516-517, 533 Data Grid 516-517, 519, 530, 536 Data Mining 178, 184-185, 187-188, 190, 195, 202, 219-220, 226, 233, 419, 422, 435, 684, 686, 716, 720, 735, 1264, 1291, 1309, 1369, 1425, 1522, 1525, 1534, 1580, 1708-1709, 1711-1713, 1733, 1740, 1755-1757, 1759-1760, 1762, 1764 dead-zone 488, 493, 1396-1397 DecisionMaking31,209-210,224,369,418-419,421, 449, 461, 476, 479, 520, 552-553, 555-557, 559- 560, 573-574, 610, 625, 647-652, 658, 684, 735, 791, 820, 828, 858, 893, 906, 924, 936, 957, 969, 978, 987, 1012, 1020, 1073, 1112, 1146, 1150- 1154, 1157, 1162-1165, 1174, 1177, 1179, 1204, 1218-1220, 1236-1237, 1253, 1309, 1330, 1350, 1354-1355, 1426, 1450, 1454, 1478-1479, 1495, 1520, 1553, 1582, 1597, 1620-1622, 1659, 1661, 1663, 1667-1670, 1673, 1681, 1693, 1708, 1710- 1713, 1718, 1720-1721, 1723, 1733-1734, 1757 Decision Support Systems 184, 682, 717, 1111, 1150, 1264, 1367, 1627, 1708, 1711, 1713 Decision Trees 188, 190, 197, 218, 222, 274, 687, 1525, 1713, 1756-1757 Deductive reasoning 1285 Defuzzification 83-84, 86, 98, 189, 192, 194, 239, 241, 284-285, 296, 329, 418, 420, 424, 430-431, 435, 442, 449, 451-452, 473, 523, 556, 571, 632, 721, 724, 752, 769, 773-774, 785, 787-788, 798, 820-821,824-825,829,947,950,962,1003-1006, 1012, 1015-1018, 1024, 1045-1046, 1049-1051, 1061, 1108, 1158-1160, 1185, 1217-1220, 1226, 1229, 1231, 1243, 1245, 1274, 1322, 1325, 1360, 1374, 1376, 1404, 1457-1458, 1529, 1564-1567, 1576, 1585, 1592, 1594, 1600-1601, 1611, 1619, 1667, 1677, 1679-1680, 1696, 1752 Defuzzification Techniques 556, 571 Degree of Membership 78, 173, 233, 325-326, 328, 368,420,472,493,521,611,635,819,979,1004, 1033, 1036, 1043, 1047, 1243, 1375, 1454, 1562, 1582, 1603, 1671-1672, 1688, 1716, 1752 Direct Matrix Converter 742-744, 762 Discernibility Matrix 1371, 1426-1427, 1437, 1450 Distance Protection 836 distribution network 989, 997 DNA Sequence 1519, 1522, 1524, 1528, 1530, 1534, 1536 DTD 1237-1238, 1240, 1247, 1249, 1251, 1263, 1267 Dynamic Data Replication 520 Dynamic Load Balancing 1643, 1663 Dynamic Performance 1397, 1420 E E-Commerce 573-574, 969-971, 982 Electroencephalogram (EEG) Signals 347-352, 355 Elitist Ant System 461, 466, 469, 475 Employee Engagement 647-649, 658 xxiii
- 26. Index Entropy 191, 273-275, 279-280, 283-284, 286, 289 Evidential Reasoning Approach 920, 932 Evolutionary Algorithm (EA) 1108 EWS 203, 207, 209, 212-214, 216, 227, 233 Expert System 184, 192, 195, 197, 202, 221-222, 224, 226, 233, 275, 418-422, 424-425, 427, 430-431, 435-436, 442, 445, 540-541, 543, 545-546, 548-549, 551-554, 684-688, 717, 721, 908-911, 919-920, 928, 933, 936, 939, 943-944, 947-948, 950-952, 954, 956-957, 967, 979, 987-993, 997, 1001, 1020, 1022, 1052, 1060, 1066, 1068, 1074, 1204, 1237-1239, 1248-1252, 1263-1264, 1273, 1285, 1368, 1600, 1667-1674, 1678-1681, 1688 F Fair Share Scheduler 321-324 Fault Detection 393-395, 405, 416, 682, 987, 989, 991, 997, 1001 Fault Isolation 416, 1184 Feature Selection 274, 280, 283-284, 286, 347, 349- 351, 353-355, 359, 361, 363, 1369 finite element 1-3, 14-15, 20, 250-252, 258-261, 264, 267, 269, 1540-1542, 1545 Finite Element Analysis 3, 15, 1540 FIS 154-155, 173-176, 179, 181-182, 189, 195, 293, 295-296, 298, 302, 305, 314-315, 422, 531, 573- 574, 582, 601, 604, 607, 611, 613-614, 616, 632- 633, 639, 641, 643, 647, 650, 652-653, 655, 722, 724, 752, 821, 947, 950-951, 1046, 1049, 1053, 1055, 1390-1392, 1457-1458, 1460-1463, 1529, 1569, 1653, 1658, 1675, 1752-1754 Fitness Function 286, 1021, 1109, 1535, 1600 FLC 312-313, 317-318, 807, 809, 818-823, 825-826, 1203-1205, 1215, 1217-1219, 1221-1222, 1228- 1229, 1231 Flexible Alternating Current Transmission Systems (FACTS) 130 FNN 78, 685-687, 707, 731, 751, 1396-1397, 1399- 1401, 1405, 1407, 1409, 1411-1412, 1414-1415, 1420, 1424 Foot Print of Uncertainty 130 Forward Chaining 557, 907, 910, 913-914, 917, 919- 920, 922-924, 932, 1267 fully fuzzy 1, 3-4, 9-10, 18, 25, 55-56, 61-62, 65, 252, 256-257, 373-375, 379-382, 385 Fully fuzzy system 1, 3-4, 9-10, 18, 25, 252, 257 Fuzzification 86, 188-189, 192, 239-241, 295, 328- 330, 418, 420, 424-425, 430, 435, 442, 447, 452, 615, 631, 721-722, 752, 769-770, 785, 793, 809, 819, 822, 827-828, 939, 947-948, 958, 1024, 1046-1047, 1051, 1074, 1108-1109, 1217-1219, 1243, 1245, 1367, 1374, 1376, 1403-1405, 1408, 1529, 1564-1565, 1576, 1584, 1600, 1667, 1674, 1677, 1680, 1752 Fuzzy Ant Colony Optimization 459, 461, 468, 479 fuzzy arithmetic 2, 56, 134, 251-253, 256, 1073-1074, 1673 Fuzzy Associative Memory 1045, 1157, 1454 Fuzzy Concept 55, 421, 523, 1061, 1404 Fuzzy Control 78, 235, 240-242, 246, 312-313, 487- 488, 515, 528, 758, 818, 820, 822, 827, 830, 938- 939, 1003, 1017-1018, 1061, 1184, 1204, 1217- 1219, 1273, 1400, 1403-1406, 1411-1413, 1669 FuzzyControlSystem758,938-939,1003,1017-1018, 1184, 1400, 1403-1404, 1412 Fuzzy Correlation coefficient 1122, 1712, 1721, 1725, 1728 Fuzzy Correlation Rule Mining 1708, 1713 Fuzzy eigenvalue problem 1, 4, 11, 20, 25 Fuzzy Expert Systems 219-220, 222-224, 226, 419, 425,442,909,935,938,943,946,950,967,1045, 1454, 1673, 1681 Fuzzy Implication 862, 1203, 1218-1220, 1229, 1236, 1644, 1649, 1672 Fuzzy Inference System 98, 132, 134, 136-137, 144- 146, 154-155, 157, 171, 173, 175, 177, 189, 192, 241, 274, 293, 295, 301, 308, 314, 324, 327, 329, 347, 354, 431, 443, 454, 523, 525-527, 531, 533, 536, 545, 547, 573, 579, 582, 594, 601-602, 604, 611, 616, 630, 633, 635, 639, 647, 650, 652-653, 655-656, 665-666, 671-672, 685, 722-724, 752, 758, 769-770, 773, 779, 781, 806, 835, 837- 838, 847, 863-864, 950-951, 1021, 1045, 1051, 1184-1186, 1356-1360, 1368, 1380, 1385, 1390, 1392-1393, 1458, 1460, 1462, 1529, 1553, 1568- 1569, 1600, 1652-1653, 1672, 1674, 1678-1679, 1681, 1752 Fuzzy Knowledge Base 194, 240, 984 fuzzy linear equations 252, 258, 261 fuzzy Logic Control 751, 807, 809, 818, 820, 1004, 1204, 1403, 1600 Fuzzy Logic Controller 239, 241, 308, 312, 314, 574, 739, 751-752, 825, 1203, 1217, 1220, 1236 xxiv
- 27. Index Fuzzy Logic Technique 738-739, 759, 762 Fuzzy Membership function 192, 556, 561, 564-565, 947, 995, 1074, 1077, 1243-1244, 1262, 1375, 1673 Fuzzy Method 650, 653, 657-658, 791 Fuzzymin-maxneuralnetwork688,692-693,705,707 Fuzzy Model 32, 133-134, 138, 146, 192, 278, 292, 295, 398-399, 419, 450, 653, 656, 658, 715, 952, 1003-1004, 1185-1186, 1374, 1390, 1596, 1598, 1600-1601, 1604, 1606, 1609, 1618-1619, 1676-1678 fuzzy numbers 2, 4-6, 14, 20, 25, 50, 55-57, 61, 134, 251-253, 255-257, 269, 367-369, 371, 373-374, 376, 380, 390, 418, 422, 425, 427-428, 435, 652, 1004, 1012, 1016-1017, 1075-1077, 1085, 1111, 1121-1122, 1139, 1478, 1480-1483, 1488, 1495-1496, 1501-1502, 1511, 1513, 1623-1624, 1631-1632, 1637, 1690-1691, 1693-1697, 1702, 1712, 1723, 1733, 1752 Fuzzy Rule Interpolation 31-33, 35, 37, 50 Fuzzyrules41,86-87,98,132,134-135,137,143-148, 155, 188, 192, 194, 197, 241, 274, 278, 284-285, 312-314, 354, 418, 420-421, 424, 428, 430, 435, 442, 461, 468, 476, 479, 493, 509, 545-546, 556, 561, 564, 574, 614, 616, 619, 631, 650, 685-686, 752, 758, 765, 769-771, 781, 785, 788, 819-820, 828, 838, 957-958, 979-981, 984, 1000, 1003- 1004, 1024, 1156-1159, 1170, 1175, 1178-1179, 1184-1186, 1231, 1242, 1244, 1246, 1248, 1262, 1273-1274, 1303, 1319-1320, 1322, 1333, 1361, 1367-1368, 1374, 1376, 1407, 1411-1412, 1457, 1529-1530, 1565-1567, 1580, 1601, 1611-1613, 1652, 1669, 1671-1674, 1676-1677, 1688 Fuzzy Set Theory 44, 80-81, 134, 155, 171, 192, 321, 324-325, 368, 421, 521, 540, 572, 938, 941-942, 989, 1003, 1045, 1073, 1075, 1085, 1156, 1184, 1270, 1342, 1398, 1425, 1427, 1454, 1457, 1479, 1597, 1622-1623, 1637, 1668-1669, 1678, 1688- 1689, 1691, 1694, 1696, 1702, 1709 Fuzzy Test System 1228, 1236 Fuzzy Theory 1-2, 420, 809, 1073, 1109, 1396, 1398, 1424, 1695-1696 Fuzzy Universal Approximation Theorem 515 FuzzyVariable427,442,461,1226,1228,1236,1332, 1562, 1565-1566, 1671, 1688 FuzzyXML1237-1239,1242,1246-1249,1251,1253, 1262-1264, 1267 FuzzyTrust 1339-1340 G Gait Analysis 1203, 1231, 1236 Gait Cycle 1205, 1208-1209, 1222-1224, 1236 Game Mechanics 765, 781 Gamification 763-765, 767-768, 778-779, 781 Gateway Periodicity 666 GenericArchitecture554,562,1237-1239,1251-1252 Generic model 1553, 1555, 1559, 1561, 1567-1568, 1573 Genetic Algorithm 34, 132, 134, 136, 236, 273-274, 277, 282, 287, 289, 293, 350, 368, 419, 460-462, 612,688,751,1020-1021,1070,1072,1079-1080, 1091, 1287, 1386, 1453, 1471, 1518, 1527, 1535- 1537, 1541, 1600, 1680 GeneticProgramming275,933,1287,1291,1535-1536 GradientDescent34,76,97,130,284,571,1286,1298 H Harvard Classification 540, 543, 545-546 Hedge 1319-1320, 1688 Hemiparesis 1205, 1210-1211, 1236 Hesitant Decision System 1426, 1431, 1437 Hesitant Information System 1426, 1431, 1434 Hierarchical Fuzzy Inference System 1356-1360 Hierarchical Fuzzy System 32-34, 42 HMI Evaluation 633, 642-643 homogeneous reactor 264, 269 Homomorphisms 1425, 1439 Homotopy perturbation method 3-4, 20, 25 Hotels Location Selection (HLS) 31, 45 Human Machine Interface 628, 630 Hybrid Intelligent System 443-445, 686, 1518-1519, 1521 Hybrid Renewable Energy System 815-816 Hybridization 86, 171, 177, 180, 444, 553-554, 560- 561, 572, 1518-1520, 1527, 1536-1538 hyperboxes689,692-693,696-698,701-703,705,707 I if-then rules 83, 155, 171, 175, 177-181, 187, 241, 289, 398-399, 428, 449, 492, 515, 521, 546, 611, 639, 650, 758, 769, 838, 862, 907, 941, 954, 979, 1043-1045, 1157, 1218, 1319, 1361, 1455, 1669, 1672, 1681 Image processing 154, 165, 274, 517, 687, 735, 957 xxv
- 28. Index imprecise knowledge 170-175, 177, 181-183, 325, 419, 1673 incremental learning 689, 692, 695, 707 Indirect Matrix Converter 742-743, 762 Induction Motor 78, 738-739, 741-742, 749, 751-753, 755, 757-760, 762 Inference Engine 83, 85, 99, 192, 241, 329-330, 492, 523, 543, 556-557, 686, 721, 798, 819-820, 838, 862,920,925,933,1017,1217,1229,1251,1267, 1356, 1360, 1376, 1600-1601, 1668, 1688, 1752 Information Retrieval 445, 1239, 1241 Intelligence Analysis 209, 1151, 1154, 1175, 1178 IntelligentControl1203-1204,1215,1236,1399,1404 Intelligent Decision Making 969, 1668 IntelligentTechniques732,735,1285,1385,1518,1537 Interval Analysis 2, 394-395, 411, 416 Intuitionistic Fuzzy Based Fair Share Scheduler 321, 324 Intuitionistic Fuzzy Inference System 327, 329 Intuitionistic Fuzzy Set 321, 324-326, 1709 Iterative methods 55, 59, 61 J JXTA-Overlay 1268-1272, 1275, 1277, 1280-1283 K k-Nearest Neighbors 1757 Knowledge-based Systems 187, 611, 920 L LEACH 609-615, 619-624, 1596-1597, 1604, 1608- 1610 Leader-Follower 1400-1402, 1414, 1424 linear equations 1, 3-4, 6-7, 9-10, 18, 25, 55-56, 252, 257-258, 261, 1185, 1189 Linguistic Variable 83, 142, 222, 226, 240, 442, 556, 565, 631, 762, 821, 824, 908, 941, 979, 1004, 1047, 1051, 1156, 1242-1243, 1245, 1262, 1273, 1407, 1671 Load Balancing 517, 663-666, 672-674, 678, 1643- 1644, 1649, 1652, 1657-1659, 1661, 1663-1664 Longitudinal wave velocity 1385-1387, 1392-1393 Low Frequency Oscillations 119, 130 Lyapunov approach 487-488 Lyapunov’s (Second) Approach 515 M Machine Learning 171, 274, 288, 292-293, 295, 298, 301, 305, 419, 424, 445, 572, 703, 716, 1190, 1285, 1302, 1306, 1309, 1367-1368, 1521-1522, 1579, 1679, 1757, 1764 MADM 652, 1110, 1112, 1146, 1710-1712 MAGDM 1478-1480, 1495, 1510, 1513, 1708, 1711- 1712, 1732-1733 Mamdani Inference System 189, 308, 604, 801, 806 Mammography 687, 707, 1023, 1061 MANET 663-666, 678 Matrix (SDU) Decomposition 515 matrix decomposition 487, 500 Max-Min Ant System 475 mechanical properties 176-177, 179, 181, 1454, 1458, 1471 Medical Applications 705, 708, 957, 1020, 1268, 1270, 1281 Medical Diagnostic 194, 968 Metastasis 1020, 1024, 1028, 1061 microstructural features 172, 174-175, 177, 181-182 Military Decision Making 1150-1153 Mobile Communications 859 Molecular Biology 1518, 1524-1525, 1537 Multi-Agent Systems (MAS) 873, 1327, 1330, 1366 Multi-Input–Multi-Output System (MIMO) 515 multilayer perceptron neural network 686-691, 699- 700, 702, 705, 707, 709 MultipleAttributeGroupDecisionMaking(MAGDM) 1478, 1495, 1708, 1711, 1720 Multiple Model Approach 393, 398-399, 411, 416 Multi-Robot System 1401, 1424 multivariable nonlinear systems 487-488 Mutual Information (MI) 347, 349, 353 N Natural Frequency 2, 24-25, 1540-1541 Neural Networks 79, 86-87, 154, 187-188, 218-219, 274, 276, 278, 282, 314, 319, 420, 444, 541, 571, 682-690, 692, 699, 703, 705, 707, 719, 808, 819, 830, 937, 963, 1184, 1186, 1198, 1291, 1295, 1298-1299, 1303, 1305, 1309, 1367, 1386, 1388, 1453-1454, 1477, 1518, 1520-1521, 1524, 1526- 1529, 1534, 1538, 1541-1542, 1545, 1548-1549, 1567, 1678, 1756-1757, 1759 xxvi
- 29. Index Neuro-Fuzzy Inference 153, 177-178, 293, 420, 443- 445, 449-450, 456, 594, 950, 1183-1184, 1192, 1197, 1390, 1457, 1542, 1545, 1740, 1752 Neuro-Fuzzy Models 155 Neuro-Fuzzy System 99, 154, 165, 179-181, 274, 445, 561-562, 569, 572, 1303, 1518, 1531, 1538, 1580 Neurological Signs 935, 951-952, 956, 968, 1680 Neuron 80, 87, 276, 450-452, 558, 690-691, 700, 707, 839, 1207-1208, 1291-1293, 1295, 1404-1405, 1408-1409, 1526-1527, 1546, 1548, 1759 neutron diffusion 252, 264, 267, 269 Nondestructive Test 1549 Nonlinear System 393, 398, 403-404, 407, 411, 416, 507, 509, 1183, 1185 Nuclear Power Plants 394, 628-629, 633, 639, 641 Number of Clusters 609-612, 614, 621, 625, 839, 1598-1600, 1606-1610, 1618 Nutrient Requirements 551, 553, 561-562 O Opinion Mining 1578 Optimal Solution 277, 279, 367-369, 375-376, 379- 380, 382, 384, 387, 390, 405, 472, 1072, 1291, 1304, 1621, 1747, 1749 OWA Operator 1481-1482 P P2P Systems 1269, 1271, 1366 Path Planning 1396-1399, 1401, 1411-1412, 1414- 1416, 1420, 1422, 1424 Peer Reliability 1268-1270, 1277-1278, 1281-1283 Petroleum 715-721, 723, 726-730, 732, 735, 815-816, 860 Phase Backup Protection 835-836, 852 Photometry 540, 543, 545 Physical Activity 763-764, 768, 778-779, 781 Physical Fitness 763-764, 774, 781 Physiographic 782, 791-793, 801, 806 physiographic characteristics 782, 801 porosity 717, 1386 power network 840, 988, 990, 997, 1001 Power Prediction 821, 825 Power System Stability 130, 235-237, 239, 244, 246 Power Systems 74-75, 77-78, 87, 235-237, 239, 242, 293-294, 840, 988-990 Principal Component Analysis (PCA) 1183-1184, 1187, 1197, 1678 Prioritization 280, 647, 651-653, 658, 1176-1178 Production Rule 442, 1672 ProjectEvaluationandReviewTechnique(PERT)1691 Prostate Cancer 935, 946, 957, 962-963, 968, 1309, 1668 protective relays 987, 990-993, 996-997, 1000 PSK Method 367, 369-370, 378-380, 382 PSO195,236,292,301-302,304-305,319,350,1070, 1072, 1074, 1079-1080, 1084, 1091-1092, 1099, 1291, 1532-1533, 1542 PSS 235-239, 241-242, 244-246, 1743 Public Procurement 1620-1622, 1627-1630, 1632, 1635, 1637-1638 PV Panels 809, 816, 829-830 PV Power 809, 816-817, 820-826, 829-830 Q Quality of Experience 1739, 1741, 1761, 1765 Quality of Service 856, 1739, 1761, 1765 R Regression Model 187, 715, 719, 724, 727-728, 735, 1388, 1746, 1765 Regression Models 715-716, 1462, 1746-1747 REGRET 1326-1327 Relative Core 1425, 1427, 1437 Relative Reduct 1372-1373, 1425, 1427, 1437 Replica Preserving Value 516, 518, 522-524, 527, 529-531, 533, 536 Replica Replacement Algorithm 516, 518-520, 522, 529-530, 536 Risk Analysis 969, 971-972, 974-975, 977, 1703 Robust Optimization 1740, 1746-1747, 1749-1750, 1765 Rotor 76, 89, 97, 117, 130, 236, 760, 762, 814, 817, 828, 1541-1542, 1544 Rough Set 171, 188, 325, 652, 1367, 1369, 1426, 1518-1519, 1534, 1538 Rough Set Theory 171, 188, 652, 1367, 1369, 1426, 1518, 1534, 1538 RoutingProtocols1596-1598,1600,1608,1614-1615, 1618 RuleBasedSystem911,919-920,928,933,1021,1600 S SaaS 460, 1364, 1643-1644 Search Engines 443-445 sector nonlinearity 493 xxvii
- 30. Index Sensor Faults 394-395, 411, 416 SentiWordNet 1576-1577, 1579, 1581-1585, 1587- 1589, 1592-1594 Service Selection 1354, 1356 Set Theory 44, 78, 80-81, 134, 155, 171, 188, 192, 321, 324-326, 368, 421, 521, 540, 572, 652, 888, 938, 941-942, 989, 1003, 1045, 1073, 1075, 1085, 1109, 1156, 1184, 1270, 1342, 1367, 1369, 1398, 1425-1427, 1454, 1457, 1479, 1518, 1534, 1538, 1597, 1622-1623, 1637, 1668-1669, 1678, 1688-1689, 1691, 1694, 1696, 1702, 1709, 1712 SGML 1239-1240, 1267 SHRM 648, 653, 658 Singular Value Decomposition 1189, 1302 Situational Understanding 1151-1154, 1162, 1166, 1170-1175, 1177-1178 Slope of the Land 782, 784, 792-794, 801, 806 SNA 202, 209, 216-221, 233 Social Network Bridge 233 Social Network Centrality 233 Social Network Distance 234 Social Network Tie Strength 234 Softcomputing154,171,274,444,569,682-684,688, 707, 715-716, 739, 749, 762, 779, 855, 957, 963, 1020, 1060, 1150, 1157, 1386-1387, 1454, 1520 Soft Computing Techniques 171, 444, 682-684, 688, 707, 715, 739, 749, 762, 779, 1150, 1454 SPORAS 1325-1327 Stars Classification 540, 543, 548 Starting theory 1553-1555, 1559, 1561 stiffness matrices 252, 259-260, 266-267, 1544 Stock-and-flow diagram 1557-1558, 1562-1563 Structural Equation Model 132-133 Structural Holes 217, 234 Style Sheet 1240, 1267 Sugeno 44, 155, 157, 177, 308, 350, 354, 398, 450, 545-546, 548, 632, 650, 752, 758, 769-770, 781, 787, 1184-1185, 1270, 1273, 1390, 1457, 1532, 1598, 1601, 1619, 1674, 1676-1677, 1753 Supervised Learning 155, 274, 558-559, 562, 572, 684,687,692,760,1298,1579,1594,1757,1759 Swam Intelligence 1518, 1538 Swarm Intelligence 1080, 1285-1286, 1288-1289, 1519-1520 Switching State 751, 762 SWOT 647-654, 656-658 System dynamics 936, 1553, 1555-1556, 1567, 1573 System Identification 298, 300, 1183-1184, 1190, 1197, 1309 T TAKAGI–SUGENO Model 1601, 1619 Temporal Logic 968 Test Case 235, 237-238, 641, 716, 719, 724, 726-729, 955 Time Series 134, 715-717, 719, 724, 730-732, 735, 950, 1185, 1190-1191, 1524, 1678 TMFC 1342, 1347 Tomosynthesis 1023, 1061 TOPSIS 1110-1112, 1123, 1129-1130, 1138-1139, 1141, 1143, 1145-1147, 1479, 1628, 1712 Training Set 187, 572, 687, 692, 695, 705, 758, 1191, 1312, 1376, 1526, 1532, 1537 Trust Calculation 1314, 1325, 1341, 1362-1364 Trust Fuzzy Comprehensive Evaluation 1348 TTL 665, 669, 673 Tweet 1588, 1591-1593 Type-1 Fuzzy Transportation Problem 374 Type-3 Fuzzy Transportation Problem 367, 369, 374 U Uncertain Parameter 395, 416 Unified Medical Language System (UMLS) 921, 933 Union 81, 83, 85, 134-135, 553, 611, 695, 824, 995, 1005, 1042, 1317-1318, 1428, 1564, 1671, 1689 Universe of Discourse 80, 83, 135, 325-326, 442, 471, 556,616,619,908,1036-1037,1047,1112,1218- 1219, 1317, 1332, 1334, 1345, 1564, 1671-1672, 1676, 1689, 1709, 1716 Urinary Incontinence 935, 943, 968 V Vague Data 287, 560, 657, 1074, 1114, 1709 Vague sets 1110-1115, 1118-1119, 1123, 1146, 1709, 1712 Value of Information 1151-1152, 1178 Variable Structure System 515 variable-structure control 494, 497 Venturini Algorithm 739, 743, 755 Verification of Expert System 933 VIKOR 1350-1354, 1356 Virtual Machine Placement 459-462, 464, 468-469, 473, 475-476, 479, 482 Voltage Regulation 75, 237, 239, 246 Voltage Source Converter 89, 739-740, 762 xxviii
- 31. Index W Weighted Averaging Operator 1482 Wind Power 807, 810, 813-814, 827-829 Wind Turbine 809, 813-815, 826-828, 830 Wireless Sensor Networks (WSN) 79, 609-611, 624, 855, 857, 1596, 1598, 1604, 1619 X XML 865, 1237-1242, 1245-1249, 1251-1253, 1262- 1264, 1267, 1714 xxix