Fuzzy Logic & Artificial Neural Network 3
- 1. Deep Learning and Soft Computing Unit 3: Soft Computing: Fuzzy Logic ---------------------------------------------------------------------------------------------------------------- Soft Computing: - Soft computing, as opposed to traditional computing, deals with approximate models and gives solutions to complex real-life problems. - It refers to a group of computational techniques that are based on artificial intelligence (AI) and natural selection. - Soft computing is based on techniques such as fuzzy logic, genetic algorithms, artificial neural networks, machine learning, and expert systems. - The techniques of soft computing are nowadays being used successfully in many domestic, commercial, and industrial applications. With the advent of the low-cost and very high- performance digital processors and the reduction of the cost of memory chips Applications of soft computing There are several applications of soft computing where it is used. Some of them are listed below: 1. It is widely used in gaming products like Poker and Checker. 2. In kitchen appliances, such as Microwave and Rice cooker. 3. In most used home appliances - Washing Machine, Heater, Refrigerator, and AC as well. 4. Apart from all these usages, it is also used in Robotics work (Emotional per Robot form). 5. Image processing and Data compression are also popular applications of soft computing. 6. Used for handwriting recognition. Elements of soft computing: 1. Fuzzy Logic: Fuzzy logic is nothing but mathematical logic which tries to solve problems with an open and imprecise spectrum of data. It makes it easy to obtain an array of precise conclusions. Fuzzy logic is basically designed to achieve the best possible solution to complex problems from all the available information and input data. Fuzzy logics are considered as the best solution finders. 2. Artificial Neural Network (ANN): Neural networks were developed in the 1950s, which helped soft computing to solve real-world problems, which a computer cannot do itself. An artificial neural network (ANN) emulates a network of neurons that makes a human brain (means a machine that can think like a human mind). Thereby the computer or a machine can learn things so that they can take decisions like the human brain.
- 2. 3. Genetic Algorithms: Genetic algorithm is almost based on nature and take all inspirations from it. There is no genetic algorithm that is based on search-based algorithms, which find its roots in natural selection and the concept of genetics. In addition, a genetic algorithm is a subset of a large branch of computation. Fuzzy Set Theory: Understanding Degrees of Membership Fuzzy Set Theory is a fundamental concept in the field of Fuzzy Logic, a branch of mathematics and computer science that deals with uncertainty and imprecision. Introduced by Dr. Lotfi Zadeh in the 1960s, fuzzy sets provide a framework for handling information that is not binary (true or false) but rather exists on a continuum between completely true and completely false. Here's an in-depth exploration of fuzzy set theory: Crisp Sets vs. Fuzzy Sets: Crisp Set: In classical set theory, a crisp set is defined using a characteristic function. Elements either belong to the set (1) or do not belong (0). For example, in a crisp set representing "Tall People," anyone over 6 feet might belong (1), and anyone shorter does not (0). Fuzzy Set: In contrast, a fuzzy set allows elements to have partial or graded membership. Each element belongs to the set to a certain degree, represented by a value between 0 and 1. In our "Tall People" example, someone who is 7 feet tall might have a membership degree of 0.9, indicating a high degree of membership, while someone who is 5 feet tall might have a membership degree of 0.3, indicating partial membership. Membership Function: Definition: A graph that defines how each point in the input space is mapped to membership value between 0 and 1. Input space is often referred to as the universe of discourse or universal set (u), which contains all the possible elements of concern in each particular application. Operations on Fuzzy Sets: Union: The union of two fuzzy sets results in a new fuzzy set where the membership degree for each element is the maximum of the memberships in the original sets. Intersection: The intersection of two fuzzy sets results in a new fuzzy set where the membership degree for each element is the minimum of the memberships in the original sets. Complement: The complement of a fuzzy set reflects the membership values around 1. If an element has a membership of 0.3 in "Not Tall," its complement in "Tall" is 0.7. Fuzzy Relations:
- 3. Fuzzy set theory can be extended to define fuzzy relations, which describe the relationships between elements in two fuzzy sets. A fuzzy relation quantifies the degree of association or compatibility between elements from the two sets using membership values. Fuzzification and Defuzzification: Fuzzification is the process of converting crisp (exact) inputs into fuzzy inputs. It assigns elements to appropriate fuzzy sets and determines their degrees of membership. Defuzzification is the process of converting fuzzy output into crisp output. It involves aggregating the fuzzy information to obtain a single, actionable value. Applications of Fuzzy Set Theory: • It is used in the aerospace field for altitude control of spacecraft and satellites. • It has been used in the automotive system for speed control, traffic control. • It is used for decision-making support systems and personal evaluation in the large company business. • It has application in the chemical industry for controlling the pH, drying, chemical distillation process. • Fuzzy logic is used in Natural language processing and various intensive applications in Artificial Intelligence. • Fuzzy logic is extensively used in modern control systems such as expert systems. • Fuzzy Logic is used with Neural Networks as it mimics how a person would make decisions, only much faster. It is done by Aggregation of data and changing it into more meaningful data by forming partial truths as Fuzzy sets. Fuzzy Logic: The term fuzzy refers to things that are not clear or are vague. In the real world many times we encounter a situation when we can’t determine whether the state is true or false, their fuzzy logic provides very valuable flexibility for reasoning. In this way, we can consider the inaccuracies and uncertainties of any situation. Fuzzy Logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1, instead of just the traditional values of true or false. It is used to deal with imprecise or uncertain information and is a mathematical method for representing vagueness and uncertainty in decision-making. Fuzzy Logic is based on the idea that in many cases, the concept of true or false is too restrictive, and that there are many shades of gray in between. It allows for partial truths, where a statement can be partially true or false, rather than fully true or false. Fuzzy Logic is used in a wide range of applications, such as control systems, image processing, natural language processing, medical diagnosis, and artificial intelligence. ARCHITECTURE
- 4. Its Architecture contains four parts : RULE BASE: It contains the set of rules and the IF-THEN conditions provided by the experts to govern the decision-making system, on the basis of linguistic information. Recent developments in fuzzy theory offer several effective methods for the design and tuning of fuzzy controllers. Most of these developments reduce the number of fuzzy rules. FUZZIFICATION: It is used to convert inputs i.e. crisp numbers into fuzzy sets. Crisp inputs are basically the exact inputs measured by sensors and passed into the control system for processing, such as temperature, pressure, rpm’s, etc. INFERENCE ENGINE: It determines the matching degree of the current fuzzy input with respect to each rule and decides which rules are to be fired according to the input field. Next, the fired rules are combined to form the control actions. DEFUZZIFICATION: It is used to convert the fuzzy sets obtained by the inference engine into a crisp value. There are several defuzzification methods available and the best-suited one is used with a specific expert system to reduce the error. Advantages of Fuzzy Logic System 1. This system can work with any type of inputs whether it is imprecise, distorted or noisy input information. 2. The construction of Fuzzy Logic Systems is easy and understandable. 3. Fuzzy logic comes with mathematical concepts of set theory and the reasoning of that is quite simple. 4. It provides a very efficient solution to complex problems in all fields of life as it resembles human reasoning and decision-making. 5. The algorithms can be described with little data, so little memory is required. Disadvantages of Fuzzy Logic Systems 1. Many researchers proposed different ways to solve a given problem through fuzzy logic which leads to ambiguity. There is no systematic approach to solve a given problem through fuzzy logic.
- 5. 2. Proof of its characteristics is difficult or impossible in most cases because every time we do not get a mathematical description of our approach. 3. As fuzzy logic works on precise as well as imprecise data so most of the time accuracy is compromised. Fuzzy systems have found extensive real-life applications: Here are some notable real-life applications of fuzzy systems: A. Automotive Industry: • Anti-lock Braking Systems (ABS): Fuzzy logic controllers in ABS adjust brake pressure based on road conditions and vehicle speed, improving safety during braking. • Transmission Control: Fuzzy systems are used to optimize gear shifts in automatic transmissions, enhancing fuel efficiency and performance. B. Consumer Electronics: • Washing Machines: Fuzzy logic controllers adapt wash cycles based on factors like load size and fabric type, ensuring optimal cleaning. • Air Conditioning: Fuzzy controllers adjust cooling and fan speed to maintain a comfortable indoor environment while minimizing energy consumption. C. Traffic Control: • Traffic Signal Optimization: Fuzzy logic is used to optimize traffic signal timings in real-time, reducing congestion and improving traffic flow. • Intelligent Transportation Systems (ITS): Fuzzy controllers manage traffic signals, variable message signs, and ramp metering to improve traffic management. D. Medicine and Healthcare: • Medical Diagnosis: Fuzzy systems assist in diagnosing diseases by considering a range of symptoms and test results with varying degrees of significance. • Drug Dosage Control: Fuzzy logic controllers are used to adjust drug dosages based on a patient's response to treatment, ensuring safety and efficacy. E. Finance: • Portfolio Management: Fuzzy systems optimize investment portfolios by considering risk tolerance and market conditions with varying levels of confidence. • Credit Scoring: Fuzzy models assess creditworthiness by considering multiple factors with varying degrees of importance. F. Robotics: • Robotic Control: Fuzzy controllers enable robots to adapt to changing environments and make decisions based on sensory input, enhancing their flexibility and autonomy. G. Environmental Control: • Environmental Monitoring: Fuzzy systems are used to assess environmental pollution levels by considering measurements with varying precision.
- 6. • Energy Management: Fuzzy logic controllers optimize energy consumption in buildings by adjusting heating, cooling, and lighting based on occupancy and comfort levels. H. Natural Language Processing (NLP): • Sentiment Analysis: Fuzzy logic techniques help analyze and categorize the sentiment of text or speech data, allowing for more nuanced sentiment assessments. I. Quality Control: • Manufacturing: Fuzzy systems are used for quality control in manufacturing processes, allowing for the detection of defects with varying levels of severity. J. Agriculture: • Irrigation Systems: Fuzzy controllers manage irrigation based on soil moisture levels and weather conditions, optimizing water usage. K. Home Appliances: • Microwave Ovens: Fuzzy controllers adjust cooking times and power levels based on the type and quantity of food, ensuring even heating.