Evolutionary Tuning of Fuzzy Rule Base Systems for Nonlinear System Modelling and Control

Fuzzy systems generally works based on expert knowledge base. Fuzzy Expert knowledge base derived from the heuristic knowledge of experts or experience operators in the form of fuzzy control rules and membership functions (MFs). The major difficulties for designing a fuzzy models and controllers are identify the optimized fuzzy rules and their corresponding shape, type and distribution of MFs. Moreover, the numbers of fuzzy control rules increases exponentially with the number of input output variables related to the control system. For this reason it is very difficult and time consuming for an expert to identify the complete rule set and shape of MFs for a complex control system having large number of input and output variables. In this paper, we propose a method called evolutionary fuzzy system for tuning the parameters of fuzzy rules and adjust the shape of MFs through evolutionary algorithms in order to design a suitable and flexible fuzzy models and controller for complex systems. This paper also presents new flexible encoding method methods for evolutionary algorithms. In evolutionary fuzzy system, the evolutionary algorithms is adapted in two different ways. Firstly, generating the optimal fuzzy rule sets including the number of rules inside it and secondly, selecting the optimum shape and distribution of MFs for the fuzzy control rules. In order to evaluate the validity and performance of the proposed approach we have designed a test strategy for the modeling and control of nonlinear systems. The simulation results show the effectiveness of our method and give better performance than existing fuzzy expert systems.