Skip to Main Content
One of the important issues in the design of fuzzy classifier is the formation of fuzzy if-then rules and the membership functions. This paper presents a Particle Swarm Optimization (PSO) approach to obtain the optimal rule set and the membership function. To develop the fuzzy system the membership functions and rule set are encoded as particles and evolved simultaneously using PSO. While designing the fuzzy classifier using PSO, the membership functions are represented as real numbers and the rule set is represented as discrete numbers. In the classification problem under consideration the objective is to maximize the correctly classified data and minimize the number of rules. This objective is formulated as fitness function to guide the search procedure to select an appropriate fuzzy classification system so that the number of fuzzy rules and the number of incorrectly classified patterns are simultaneously minimized. The performance of the proposed approach is demonstrated through development of fuzzy classifier for Iris data available in UCI machine learning repository and Simulation results show the suitability of the proposed approach for developing the fuzzy system.