A Framework for Identification of Fuzzy Models through Particle Swarm Optimization Algorithm

This paper presents a framework for the identification of fuzzy models from the available input-output data through Particle Swarm Optimization (PSO) algorithm. Like other evolutionary algorithms, PSO is a population-based stochastic algorithm and is a member of the broad category of swarm intelligence techniques based on metaphor of social interaction. The suggested framework has the capability to identify optimized Mamdani and Singleton fuzzy models. For the presentation and validation of the proposed framework, the data from the rapid Nickel-Cadmium (Ni-Cd) battery charger developed by the authors has been used.