Generating Fuzzy Rules from Examples Using the Particle Swarm Optimization Algorithm

The use of fuzzy logic to solve control problems have been increasing considerably in the past years. The problem of generating desirable fuzzy rules is very important in the development of fuzzy systems. It is known that the fuzzy control rules for a control system is always built by designers with trial and error and based on their experience or some experiments. This paper presents a generation method of fuzzy rule by learning from examples using the Particle Swarm Optimization method (PSO). The proposed algorithm can obtain a set of fuzzy rules which cover the examples set in iterative process. The proposed method is tested with promising results.