Fuzzy model identification for rapid nickel-cadmium battery charger through particle swarm optimization algorithm

Abstract

This paper presents the fuzzy model identification for rapid Nickel-Cadmium (Ni-Cd) battery charger by applying Particle Swarm Optimization (PSO) algorithm on the input-output data. Models generated through this approach provide the flexibility of black-box approach like neural networks, since it does not need to know any information regarding the process that generates the data. The PSO method is a member of the broad category of swarm intelligence techniques for finding optimized solutions. The motivation behind the PSO algorithm is the social behavior of animals viz. flocking of birds and fish schooling and has its origin in simulation for visualizing the synchronized choreography of bird flock. The data for the batteries charger was obtained through experimentation with an objective to charge the batteries as fast as possible. The implementation of the approach is described and simulation results are presented to illustrate its effectiveness.