Application of particle swarm optimization algorithm for weighted fuzzy rule-based system

The particle swarm optimization (PSO) algorithm is an evolutional optimization method. Some of the attractive features of the PSO algorithm include its easy implementation and the fact that no gradient information is required. In this paper, a weighted fuzzy rule-based system has been designed, in which the parameters of membership functions including position and shape of the fuzzy rule set and weights of rules are estimated using the PSO algorithm. The efficiency of the system has been illustrated in the process of classifying the iris data. This paper also shows that weighted fuzzy rules can lead to better fuzzy system compared with non-weighted fuzzy rules.