FUZZY GENETIC HYBRID SYSTEM of neural system.pptx

Its a hybrid system inform

1. FUZZY GENETIC HYBRID SYSTEM Submitted to:- Submitted by:- Dr. Lini Mathew Priyanka(221515) Professor and HOD Ram Swaroop Bishnoi(221516) Electrical Engineering Department M.E Modular(I&C) Electrical Engineering NITTTR Chandigarh NITTTR Chandigarh

2. Contents Introduction to Fuzzy logic Merits and Demerits of Fuzzy logic Introduction to Genetic Algorithm Merits and Demerits of Genetic Algorithm Types of Hybrid Systems Limitations of the traditional Optimization Fuzzy- Genetic Hybrid System Structure of Fuzzy- Genetic Hybrid System Advantages and Limitations Applications Heart Disease Prediction Using Hybrid Genetic Fuzzy Model A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System References

3. Definition of fuzzy Fuzzy – “not clear, distinct, or precise; blurred” Definition of fuzzy logic A form of knowledge representation suitable for notions that cannot be defined precisely, but which depend upon their contexts. Compared to traditional binary sets fuzzy logic variables may have a truth value that ranges in degree between and 1 Membership Function The membership function represents the degree of truth as an extension of valuation. Introduction Of Fuzzy Logic

4. 4 The term "fuzzy logic" was introduced with the 1965 proposal of fuzzy set theory by Lotfi A. Zadeh. Fuzzy logic has been applied to many fields, from control theory to artificial intelligence. Fuzzy logics however had been studied since the 1920s as infinite- valued logics notably by Łukasiewicz and Tarski

5. Fuzzy Logic resembles the human decision-making methodology. It deals with vague and imprecise information. In fuzzy logic, everything is a matter of degree. Fuzzy Logic

6. A point on that scale has three "truth values"— one for each of the three functions. red arrow points to zero, this temperature may be interpreted as "not hot“ The orange arrow (pointing at 0.2) may describe it as "slightly warm“ The blue arrow (pointing at 0.8) "fairly cold" Fuzzy Truth Value

7. Sensor Fuzzification Fuzzy Inference System to be controlled Defuzzification Membership function of input fuzzy set Rule Base Membership function of output fuzzy set Feedback Fuzzy Logic Controller

8. Merits of Fuzzy Logic Handling Uncertainty: Provides a framework for handling uncertainty and imprecision in data. Linguistic Representation: Allows us to use linguistic variables and terms to describe concepts. Flexibility : Offers flexibility in dealing with complex and nonlinear relationships. Fault Tolerance : Robust and tolerant to noise and imprecise inputs. Smooth Transitions : Enables smooth transitions between different states or levels. Interpretable Results : Systems produce results that are interpretable and explainable.

9. Demerits of Fuzzy Logic Complexity of Rule Base Computational Overhead Difficulty in Knowledge Acquisition Lack of Interpretability Sensitivity to Input Parameters Lack of Formal Mathematical Foundation

10. Genetic algorithm is a population-based probabilistic search and optimization techniques, which works based on the mechanisms of natural genetics and natural evaluation. In simple words, they simulate “ survival of the fittest ” among individual of consecutive generation for solving a problem.  GA is an iterative process. It is a searching technique. Working cycle with / without convergence. Solution is not necessarily guaranteed. Usually, terminated with a local optima. Genetic Algorithms

11. A genetic algorithm (or GA ) is a search technique used in computing to find true or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination). 11 Genetic Algorithm

12. Evolution takes place taking account of each and every member of a species. Thus when we are going to search a solution in a solution space of a given problem, each solution will be an individual. A no of all solutions in an iteration no ‘I’ will be the total population and each iteration is similar to a generation. In any generation children or off- springs are made due to reproduction of parents from previous generation. Cross- over and mutation are taken place while reproduction occurs. In this generation again natural selection select two parents for reproduction and create off- springs of the next generation and so on until the selection procedure ends. This method continues until solution criteria is satisfied. Simulation continued

13. Flow Chart Of Genetic Algorithm

14. Step1: Start with a randomly generated population of n chromosomes Step2: Calculate the fitness f(x) of each chromosome x in the population. Step3: Repeat the following steps until n offspring have been created, Selection : Roulette- wheel selection operation. (Or, any) Crossover : With Cross- over probability p_cross - over. 3. Mutation: With Mutation Probability p_mutation . Step4: Replace the current population with the new population. Step5: Repeat steps 2-4 until the objective's solution is found. GA Algorithm Steps

15. Cossover is a genetic operator used to vary the programming of a chromosome or chromosomes from one generation to the next. It is analogous to reproduction and biological crossover, upon which genetic algorithms are based. Cross over is a process of taking more than one parent solutions and producing a child solution from them. 15

16. Crossover one- point crossover parent A parent B 1 1 1 1 1 offspring A offspring B 1 1 1 1 1 two- point crossover parent A parent B 1 1 1 1 1 offspring A offspring B 1 1 1 1 1

17. Mutation is a genetic operator used to maintain genetic diversity from one generation of a population of genetic algorithm chromosomes to the next. It is analogous to biological mutation. Mutation alters one or more gene values in a chromosome from its initial state. In mutation, the solution may change entirely from the previous solution. Hence GA can come to better solution by using mutation. Mutation occurs during evolution according to a user-definable mutation probability. This probability should be set low. If it is set too high, the search will turn into a primitive random search. Mutation

18. Optimization is an effort to find the best solutions for a given problem (often Multi- dimensional) from a no of alternative solutions in a search space. Sometimes we need to maximize or minimize some function say maximizing the profit or minimizing the cost, path length etc which depends upon various parameters. Mathematical Example: say, f=f( x1, x2, x3 … xn ), We want to find maximum value of ‘f’ and for that the values of x1,x2 … xn and suppose if x1 increases upto some value ‘f’ also increases and if for x2 decreases ‘f’ decreases and also values of x1,x2,x3… xn effect each other so to find the maxima basically we need a ‘ TRADE- OFF ‘ between the values of variables that’s called optimization. Optimization

19. Mathematical Examples:

20. Different Important Optimization Techniques

21. Nature t o Computer Mapping NATURE INDIVIDUAL POPULATION FITNESS CHROMOSOME GENE CROSSOVER MUTATION NATURAL SELECTION GENERATION COMPUTER Solution to a problem A no of trial solution The Quality of Solution Encoding for a Solution Part of encoding of a solution that can interchange with others Search Operators Search Operators help to avoid local optimas Reuse of Good solutions Iteration

22. Genetic algorithms are good at taking larger, potentially huge, search spaces and navigating them looking for optimal combinations of things and solutions which we might not find in a life time. Genetic algorithms are very different from most of the traditional optimization methods. Genetic algorithms need design space to be converted into genetic space. So, genetic algorithms work with a coding of variables. The advantage of working with a coding of variable space is that coding discretizes the search space even though the function may be continuous. A more striking difference between genetic algorithms and most of the traditional optimization methods is that GA uses a population of points at one time in contrast to the single point approach by traditional optimization methods. This means that GA processes a number of designs at the same time. As we have seen earlier, to improve the search direction in traditional optimization methods, transition rules are used and they are deterministic in nature but GA uses randomized operators. Random operators improve the search space in an adaptive manner. Merits of Genetic Algorithms

23. Take more space to calculate fitness every- time. Often in practical situation if mutation probability is high it fails to give good solution and again takes larger space. Tendency to converge towards local optima if mutation is not proper. Cant operate efficiently on a dynamic set of data and when it was already tense to converge towards a solution for previous situation. Not always superior to other optimization problems. Its situation dependent so We cant use this technique blindly. Demerits of Genetic algorithms

24. Types of Hybrid Systems in Soft Computing Sequential Hybrid Systems As the name suggests, this system exhibits a pipe-like flow structure It is also the weakest hybrid system Sequential Hybrid System is characterized by a very straightforward structure and exhibits integration or congregation of a variety of technologies. GA preprocessor is an ideal example of these types of systems.

25. Auxiliary Hybrid System In this system a particular technologies includes the other technology as a subroutine. This subroutine technology can called again to process information that will be used further in the overall system.  Examples include neuro-genetic system which uses Genetic algorithms to optimize the neural networks input parameters An Auxiliary Hybrid System works like this

26. Embedded Hybrid Systems Embedded hybrid systems undertake an intertwined process flow in soft computing. In this type of system no technology can be used without the involvement of other hybrid technologies. Most popular example is the Neural Network and Fuzzy Logic hybrid system.  An Embedded Hybrid System’s basic functionality is displayed in the following image

27. Computationally expensive. For a discontinuous objective function, methods may fail. Method may not be suitable for parallel computing. Discrete (integer) variables are difficult to handle. Methods may not necessarily adaptive. Evolutionary algorithms have been evolved to address the above mentioned limitations of solving optimization problems with traditional approaches. Limitations of the traditional optimization

28. Hybrid systems of fuzzy logic refer to the integration of fuzzy logic with other computational or control techniques to enhance the capabilities or performance of the system. A Fuzzy Genetic Hybrid System is developed to use fuzzy logic-based techniques for improving and modeling Genetic algorithms and vice-versa, the results obtained have been called fuzzy genetic algorithms (FGAs). Genetic algorithm has proved to be a robust and efficient tool to perform tasks like generation of the fuzzy rule base, optimization of fuzzy rule bases, generation of membership function, and tuning of membership functions . GA is found to be an efficient tool for global optimization but its local search capability is seen to be poor. On the other hand, an FLC ( Fuzzy Logic Controller) is a powerful tool for local search . Therefore, the global search power of a GA may be combined with the local search capability of an FLC to develop an FGA. Fuzzy- Genetic Hybrid System

29. A fuzzy fitness finding mechanism guides the GA through the search space by combining the contributions of various criteria/features that have been identified as the governing factors for the formation of the cluster. All these tasks can be considered as optimization or search processes within large solution spaces. Moreover, the performance of a GA depends on its parameters, such as probability of crossover, probability of mutation, population size etc. and an FLC may be used to control these parameters. The application of GAs in various optimization and search problems involving fuzzy systems. Fuzzy- Genetic Hybrid System

30. FUZZY GENETIC HYBRID SYSTEM

31. I ntegration of Fuzzy and GA can be done in two ways By the use of fuzzy logic based techniques for improving genetic algorithm behaviour and modelling GA components. This is called as fuzzy genetic algorithms ( FGAs). 2. By the application of genetic algorithms in various optimization and search problems involving fuzzy systems. • An FGA is considered as a GA that uses techniques or tools based on fuzzy logic to improve the GA behaviour modelling. For ex: fuzzy operators and fuzzy connectives for designing genetic operators with different properties, fuzzy logic control systems for controlling the GA parameters according to some performance measures or stopping criteria etc. • GAs are utilized for solving different fuzzy optimization problems, for ex: fuzzy optimization of distribution networks, fuzzy optimal reliability design problems etc

32. Fuzzy- Genetic Hybrid System flow chart

33. Advantages:  GAs are used to develop the best set of rules to be used by a fuzzy inference engine. GAs are used to optimize the choice of membership functions. A Fuzzy GA is a directed random search over all discrete fuzzy subsets. It can mimic the human decision-making process. Disadvantages:  Interpretation of results is difficult. Difficult to build membership values and rules. Takes lots of time to converge.

34. Mechanical Engineering Electrical Engine Artificial Intelligence Economics Applications : 

35. The objective of this work is to design a model that can help in predicting whether an incoming patient has heart disease or not. Genetic algorithms help in medical mining using their stochastic searching technique, the fitness function along with a set of genetic operators. The fuzzy logic is a tool for providing solution to the problems that deal with fuzzy input data. The proposed work takes into account the Genetic Algorithm (GA) for feature selection, and fuzzy logic for classification. Fuzzy classification is a supervised learning method where the fuzzy model understands the data with its rules and class label of the training data and predicts the target value for the set of test data. A fitness function evaluates the fitness of a chromosome in a population. A chromosome is the characteristic of the data. The design of fitness function (how close a given solution is to the optimum solution) is fundamental for the genetic algorithm, as it decides the termination criterion. Heart Disease Prediction Using Hybrid Genetic Fuzzy Model

36. Membership Functions Fuzzy Rule Generation

37. A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System Objective of an intelligent traffic signal system is to increase the work efficiency of these traffic signals in order to reduce traffic jams and congestions as well as vehicle emissions in the metropolitan area. The system proposed in this paper is that the number of vehicles on the road is counted by a video image detection object system .Then we will apply those outputs as inputs to our system, composed of FLCs with a set of fuzzy rules . The flexible and robust nature of the developed fuzzy controller allows it to model functions of arbitrary complexity while at the same time being inherently highly tolerant to imprecise data . Our system applies Fuzzy Logic Controllers together with Genetic Algorithm (FLCGA) to four intersections with four directions controlled by traffic signal controllers. The results of our system show an improvement in the performance using the FLCGA rather than traditional Fuzzy Logic Controller, FLC, and also rather than Conventional Traffic Signal Controller, CTC.

38. Advances in Fuzzy System

39. References Santhanam, T., & Ephzibah , E. P. (2015). Heart disease prediction using hybrid genetic fuzzy model. Indian Journal of Science and Technology, 8(9), 797–803. S. M. Odeh, A. M. Mora, M. N. Moreno, J. J. Merelo , "A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System",  Advances in Fuzzy Systems , vol. 2015, Article ID 378156, 11 pages, 2015. https://doi.org/10.1155/2015/378156 Kaur, J., Khehra , B.S. Fuzzy Logic and Hybrid based Approaches for the Risk of Heart Disease Detection: State-of-the-Art Review.  J. Inst. Eng. India Ser. B   103 , 681–697 (2022). https://doi.org/10.1007/s40031-021-00644-z Ruan , D. (1997). Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms.