Immune Genetic Learning Using Simulating Evolution in Automatic Fuzzy Systems Synthetics

In this paper we present an immune genetic learning method for the automatic synthesis of a class of fuzzy systems with variable number of rules of specific form, namely Dynamic Self-Organizing Fuzzy Systems (DSOFSs). The goal of the immune genetic synthesis is the search of an optimal set of such rules when synthesizing a fuzzy system for a specific problem. This optimal set of rules must reach for some desired features of the fuzzy system (such as minimal number of rules etc.) In order to reach this goal the proposed immune genetic learning method uses some unequal operator which allow the synthesis of the variable structure of the fuzzy system. The results of the immune genetic synthesis in a pattern recognition problem are presented. The results emphasize the impact of the specific immune genetic learning method over the desired characteristics of the fuzzy system.