Genetic Fuzzy System

Chapter 2 gives a brief introduction of fuzzy logic, genetic algorithms, and several other concepts used in the formulation of the genetic fuzzy system. Concepts such as fuzzy sets, fuzzy rule base, inference engine, linguistic measure, universe of discourse, fuzzification, and defuzzification are explained. The concept of converting numbers into words using fuzzy membership functions, which is critical in fuzzy logic, is illustrated. The genetic algorithm is introduced as an optimization method used for maximizing an objective function. The genetic algorithm is based on the survival of the fittest theory from evolution. The selection of a starting population and a mating pool is explained. The genetic operators of crossover and mutation are illustrated with examples. The use of the genetic algorithm to create optimal fuzzy logic systems by maximizing the success rate of fault isolation is explained. The genetic fuzzy system is the algorithmic tool which is used for fault isolation in this book.