PARAMETER OPTIMIZATION WITH GENETIC FUZZY SYSTEMS

Abstract

Genetic fuzzy systems are hybrid systems in artificial intelligence, which are created to combine the advantages of fuzzy systems and genetic algorithms. The degree of flexibility, that genetic algorithms implement, makes them suitable for optimization of fuzzy
systems and development of systems for decision-making related to the diagnosis, monitoring and control. This article briefly traced the change of the concept of mathematical modeling, as well as basic concepts of artificial intelligence. Publishing reference and citation activity in these areas is shown, based on data from Web of Science. The main types of fuzzy systems are presented, as well as the concept of genetic fuzzy system. Successful interaction of genetic algorithms with existing simulations and models is realized on a previously created fuzzy rule-based system FSSAM. Part of the source code is displayed and some results from the application of the fuzzy genetic system for parameters optimization are presented.