![International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD30186 | Volume – 4 | Issue – 2 | January-February 2020 Page 885
The algorithms can be described with little data, so little
memory is required.
3. DISADVANTAGES OF FUZZY LOGIC SYSTEMS
Many researchers proposed different ways to solve a given
problem through fuzzy logic which lead to ambiguity. There
is no systematic approach to solve a given problem through
fuzzy logic.
Proof of its characteristics is difficult or impossible in most
cases because every time we do not get mathematical
description of our approach.
As fuzzy logic works on precise as well as imprecise data so
most of the time accuracy is compromised.
4. DEVELOPMENT OF DYNAMIC LOAD MODEL
The medium voltage cascaded H-bridge multi-level inverter
drives are one of the topologies for high power applications.
The drive is built utilizing a series of low voltage control
modules. Typically, 9 control modules shape a 18-pulse
system, and 12 control modules shape a 24-pulse system at
the drive input. The topology of a 9-control module18-pulse
medium voltage drive can be found. For these 18-pulse
drives, there are three power modules in a phaseleg,andthe
drives can create as much as 1,440 V line-to-neutral,or,then
again 2,494 V line-to-line at the yield. The topologyofa nine-
control module 18-pulse medium voltage drive and an
enlistment motor system is appeared in Figure 1(a).
Distribution of forces between upper and lower torso 5.3.1
L5/S1 shear force
In his book on low back disorders, McGill (2002)
summarized a list of risk factors for low back disordersfrom
a review of epidemiological and tissue based studies. This
composite list includes “static posture...specifically
prolonged trunk flexion and a twisted or laterally bent
trunk” and “peak and cumulative low back shear force,
compression force and extensor moment.” Static posture,
trunk flexion and exposure to low back shear are typical in
most backpack load carriage situations. In Figure 16, shear
force is in the Y direction and vertical force is in the Z
direction. The medial lateral shear in the X direction is
approximately 0, indicating that the load is balanced side to
side. Torques can also be plotted in the same way.
5. CRITICAL FACTOR OUTPUTS
The concept of critical factor output pertains to creating
analysis capabilitiesthatevaluateknown mechanisms where
injury risk modes have been documented. Model output
refinement will be directed at reflecting the degree of
potential peril a user is experiencing given the physical
demands of the mobility tasks. This concept of determining
the potential injury modes and creating a model to
determine the risk state of the humansubjectisapplicableto
a variety of human device interfaces. The software permits
the calculation of contact forces, displacements of bodies,
forces in constraints and internal stresses using linear finite
element analysis. Given a prescribed velocity profile, the
model currently calculates:
X, Y and Z contact force between the pack and the body
Distribution of load between the upper and lower torso
Shoulder strap forces in the upper and lower straps
Estimated L4/L5 compression, shear and torque
3D displacement of the subject
3D displacement of the pack
6. FUZZIFICATION
Fuzzification is the process of assigning the numerical input
of a system to fuzzy sets with some degree of membership.
This degree of membership may be anywhere within the
interval [0,1]. If it is 0 then the value does not belong to the
given fuzzy set, and if it is 1 then the value completely
belongs within the fuzzy set. Any value between 0 and 1
represents the degree of uncertainty that the value belongs
in the set. These fuzzy sets are typically described by words,
and so by assigning the system input to fuzzy sets, we can
reason with it in a linguistically natural manner.
Fuzzy logic operators
Fuzzy logic works with membership values in a way that
mimics Boolean logic. To this end, replacements for
basic operators AND, OR, NOT must be available. There are
several ways to this. A common replacement is called
the Zadeh operators:
Boolean Fuzzy
AND(x,y) MIN(x,y)
OR(x,y) MAX(x,y)
NOT(x) 1 – x
For TRUE/1 and FALSE/0,thefuzzyexpressionsproduce the
same result as the Boolean expressions.
There are also other operators, more linguistic in nature,
called hedges that can be applied. These are generally
adverbs such as very, or somewhat, which modify the
meaning of a set using a mathematical formula](https://image.slidesharecdn.com/152fuzzylogic-200514102728/85/Fuzzy-Logic-Based-Parameter-Adaptation-of-Interior-Search-Algorithm-2-320.jpg)
![International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID – IJTSRD30186 | Volume – 4 | Issue – 2 | January-February 2020 Page 887
Thedesignedsystemcanbeextendedforanynumberofinputs and
outputs. The drain valve control output can be utilized
further for land irrigation according to the need and water
release control valve for electric generation to fulfill thedire
need of this system in automation
The exactness of the proposed demonstrate is verified by a
contextual analysis utilizing a specimen medium voltage
motor drive system. The influence of key parameters of the
model on unique reaction qualities is assessed through an
affectability think about. The created dynamic load model of
the medium voltage motor drive systemiscommunicated by
seventh request exchange
12. REFERENCES LINKS
[1] www.francky.me/doc/course/fuzzy_logic.pdf
[2] www.tutorialspoint.com/fuzzy_logic/fuzzy_logic...
[3] apps.dtic.mil/dtic/tr/fulltext/u2/a481125.pdf
[4] www.sciencedirect.com/.../inference-process
[5] http://mrmc-
www.army.mil/index.asp?EntryURL=/mrdRADs.asp
[6] www.academia.edu/8007234/Journal_fuzzy_logic_PDF](https://image.slidesharecdn.com/152fuzzylogic-200514102728/85/Fuzzy-Logic-Based-Parameter-Adaptation-of-Interior-Search-Algorithm-4-320.jpg)
Fuzzy Logic Based Parameter Adaptation of Interior Search Algorithm
- 1. International Journal of Trend in Scientific Research and Development (IJTSRD) Volume 4 Issue 2, February 2020 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470 @ IJTSRD | Unique Paper ID – IJTSRD30186 | Volume – 4 | Issue – 2 | January-February 2020 Page 884 Fuzzy Logic M. Bhuvaneswari, P. Daniel Samson, V. Anish Sri Krishna Adithya College of Arts and Science, Kovai Pudur, Coimbatore, Tamil Nadu, India ABSTRACT This paper proposes a detailed switching model for the medium voltage cascaded H-bridge multi-level inverter drive and induction motor system using fuzzy logic controller which is suitable for power system dynamic studies. The model includes the We describe in this book recent advances in the fuzzy logic based augmentation of neural networks and in optimization algorithms and their application in areas fuzzy logic can help design robust individual behaviours units. Fuzzy logic controllers incorporate heuristic control knowledge. It is convenient choice when a precise linear model of the system to be controlled cannot be easily found. Another advantage of fuzzy logic control is to use fuzzy logic for representing uncertainties, such as vagueness or imprecision which cannot be solved by probability theory. Also fuzzy logic offers greater flexibility to user, among which we can choose the one that best, fits the type of combination to be performed. How to cite this paper: M. Bhuvaneswari | P. Daniel Samson | V. Anish "FuzzyLogic" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456- 6470, Volume-4 | Issue-2, February 2020, pp.884-887, URL: www.ijtsrd.com/papers/ijtsrd30186.pdf Copyright © 2019 by author(s) and International Journal ofTrendinScientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (CC BY 4.0) (http://creativecommons.org/licenses/by /4.0) 1. INRODUCTION IT HAS been perceived for over two decades that portrayals of energy system loads for dynamic performance examination can have significant effect on control system soundness. As power systems are planned and worked with a lower soundness edge, sufficient load models are of major significance. Regardless of colossal researchendeavoursand gained learning, stack demonstrating stays a standout amongst the most questionable ranges in huge scale control system reproductions due to the changing idea of burdens and the development of new sorts of burdens, for example, factor recurrence drives. FuzzydynamicprogrammingmodelwasusedforHarkeddaminthe State of Orissa in India in which irrigation; hydropower generation and flood control were considered as fuzzy variables. The neural network and fuzzy systems were also adopted or dam control in which comparison was made between reservoir operations using the fuzzy and neural network systems and actual one byoperator,using examples offloods during flood and non-flood seasons. It is a technique to embody human-like thinking into a control system. It may not be designed to give accurate reasoning but it is designed to give acceptable reasoning. It can emulate human deductive thinking,thatis,theprocess people use to infer conclusions from what they know. Any uncertainties can be easily dealt with the help of fuzzy logic. 2. ADVANTAGES OF FUZZY LOGIC SYSTEM This system can work with any type of inputs whether it is imprecise, distorted or noisy input information. The construction of Fuzzy Logic Systems is easy and understandable. Fuzzy logic comes with mathematical concepts of set theory and the reasoning of that is quite simple. It provides a very efficient solution to complex problems in all fields of life as it resembleshumanreasoninganddecision making. IJTSRD30186
- 2. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD30186 | Volume – 4 | Issue – 2 | January-February 2020 Page 885 The algorithms can be described with little data, so little memory is required. 3. DISADVANTAGES OF FUZZY LOGIC SYSTEMS Many researchers proposed different ways to solve a given problem through fuzzy logic which lead to ambiguity. There is no systematic approach to solve a given problem through fuzzy logic. Proof of its characteristics is difficult or impossible in most cases because every time we do not get mathematical description of our approach. As fuzzy logic works on precise as well as imprecise data so most of the time accuracy is compromised. 4. DEVELOPMENT OF DYNAMIC LOAD MODEL The medium voltage cascaded H-bridge multi-level inverter drives are one of the topologies for high power applications. The drive is built utilizing a series of low voltage control modules. Typically, 9 control modules shape a 18-pulse system, and 12 control modules shape a 24-pulse system at the drive input. The topology of a 9-control module18-pulse medium voltage drive can be found. For these 18-pulse drives, there are three power modules in a phaseleg,andthe drives can create as much as 1,440 V line-to-neutral,or,then again 2,494 V line-to-line at the yield. The topologyofa nine- control module 18-pulse medium voltage drive and an enlistment motor system is appeared in Figure 1(a). Distribution of forces between upper and lower torso 5.3.1 L5/S1 shear force In his book on low back disorders, McGill (2002) summarized a list of risk factors for low back disordersfrom a review of epidemiological and tissue based studies. This composite list includes “static posture...specifically prolonged trunk flexion and a twisted or laterally bent trunk” and “peak and cumulative low back shear force, compression force and extensor moment.” Static posture, trunk flexion and exposure to low back shear are typical in most backpack load carriage situations. In Figure 16, shear force is in the Y direction and vertical force is in the Z direction. The medial lateral shear in the X direction is approximately 0, indicating that the load is balanced side to side. Torques can also be plotted in the same way. 5. CRITICAL FACTOR OUTPUTS The concept of critical factor output pertains to creating analysis capabilitiesthatevaluateknown mechanisms where injury risk modes have been documented. Model output refinement will be directed at reflecting the degree of potential peril a user is experiencing given the physical demands of the mobility tasks. This concept of determining the potential injury modes and creating a model to determine the risk state of the humansubjectisapplicableto a variety of human device interfaces. The software permits the calculation of contact forces, displacements of bodies, forces in constraints and internal stresses using linear finite element analysis. Given a prescribed velocity profile, the model currently calculates: X, Y and Z contact force between the pack and the body Distribution of load between the upper and lower torso Shoulder strap forces in the upper and lower straps Estimated L4/L5 compression, shear and torque 3D displacement of the subject 3D displacement of the pack 6. FUZZIFICATION Fuzzification is the process of assigning the numerical input of a system to fuzzy sets with some degree of membership. This degree of membership may be anywhere within the interval [0,1]. If it is 0 then the value does not belong to the given fuzzy set, and if it is 1 then the value completely belongs within the fuzzy set. Any value between 0 and 1 represents the degree of uncertainty that the value belongs in the set. These fuzzy sets are typically described by words, and so by assigning the system input to fuzzy sets, we can reason with it in a linguistically natural manner. Fuzzy logic operators Fuzzy logic works with membership values in a way that mimics Boolean logic. To this end, replacements for basic operators AND, OR, NOT must be available. There are several ways to this. A common replacement is called the Zadeh operators: Boolean Fuzzy AND(x,y) MIN(x,y) OR(x,y) MAX(x,y) NOT(x) 1 – x For TRUE/1 and FALSE/0,thefuzzyexpressionsproduce the same result as the Boolean expressions. There are also other operators, more linguistic in nature, called hedges that can be applied. These are generally adverbs such as very, or somewhat, which modify the meaning of a set using a mathematical formula
- 3. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD30186 | Volume – 4 | Issue – 2 | January-February 2020 Page 886 IF-THEN rules Main article: Fuzzy rule IF-THEN rules map input or computed truth values to desired output truth values. Example: IF temperature IS very cold THEN fan_speed is stopped IF temperature IS cold THEN fan_speed is slow IF temperature IS warm THEN fan_speed is moderate IF temperature IS hot THEN fan_speed is high 7. FUZZY INFERENCE PROCESS Fuzzy inference is the process of formulating the mapping from a given input to an output using fuzzy logic. The mapping then provides a basis from which decisions can be made, or patterns discerned. The process of fuzzy inference involves all the pieces that are described in Membership Functions, Logical Operations, and If-Then Rules. This section describes the fuzzy inference process and uses the example of the two-input, one-output, three-ruletipping problem from The Basic Tipping Problem. The basic structure of this example is shown in the following diagram: 8. AGGREGATE ALL OUTPUTS Since decisions are based on testing all the rules in a FIS, the rule outputs mustbecombinedinsomemanner.Aggregation is the process by which the fuzzy sets that represent the outputs of each rule are combined into a single fuzzy set. Aggregation only occursoncefor eachoutputvariable,which is before the final defuzzification step. The input of the aggregation process is the list of truncated output functions returned by the implication processforeachrule.Theoutput of the aggregation process is one fuzzy set for each output variable. As long as the aggregation method is commutative, then the order in which the rules are executed is unimportant. Three built-in methods are supported: max (maximum) probor (probabilistic OR) sum (sum of the rule output sets) In the following diagram, all three rules are displayed to show how the ruleoutputsareaggregatedintoa single fuzzy set whose membership function assigns a weighting for every output (tip) value. 9. DEFUZZIFY The input for the defuzzification process is a fuzzy set (the aggregate output fuzzy set) and the output is a single number. As much as fuzziness helps the rule evaluation during the intermediate steps, the final desired output for each variable is generally a single number. However, the aggregate of a fuzzy set encompasses a range of output values, and so must be defuzzified to obtain a single output value from the set. There are five built-in defuzzification methods supported: centroid, bisector, middle of maximum (the average of the maximum value of the output set), largest of maximum, and smallest of maximum. Perhaps the most popular defuzzification method is the centroid calculation, which returns the center of area under the curve, as shown in the following: 10. FUZZY INFERENCE DIAGRAM The fuzzy inference diagram is the composite of all the smaller diagrams presented so far in this section. It simultaneously displays all parts of the fuzzy inference process you have examined. Information flows through the fuzzy inference diagram as shown in the following figure. 11. CONCLUSION The dynamic load display for a medium voltage cascaded H- bridge multi-level PWM inverter motor drive system is created in this paper, which is inferred utilizing a scientific strategy called the linearization approach.
- 4. International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 @ IJTSRD | Unique Paper ID – IJTSRD30186 | Volume – 4 | Issue – 2 | January-February 2020 Page 887 Thedesignedsystemcanbeextendedforanynumberofinputs and outputs. The drain valve control output can be utilized further for land irrigation according to the need and water release control valve for electric generation to fulfill thedire need of this system in automation The exactness of the proposed demonstrate is verified by a contextual analysis utilizing a specimen medium voltage motor drive system. The influence of key parameters of the model on unique reaction qualities is assessed through an affectability think about. The created dynamic load model of the medium voltage motor drive systemiscommunicated by seventh request exchange 12. REFERENCES LINKS [1] www.francky.me/doc/course/fuzzy_logic.pdf [2] www.tutorialspoint.com/fuzzy_logic/fuzzy_logic... [3] apps.dtic.mil/dtic/tr/fulltext/u2/a481125.pdf [4] www.sciencedirect.com/.../inference-process [5] http://mrmc- www.army.mil/index.asp?EntryURL=/mrdRADs.asp [6] www.academia.edu/8007234/Journal_fuzzy_logic_PDF