Optimization of type-2 fuzzy inference systems using GAs and PSO are presented.Optimized type-2 fuzzy systems are used to estimate the type-2 fuzzy weights.Simulation results and a comparative study are presented to illustrate the method.Bio-inspired optimization of the type-2 fuzzy systems is viable for this problem. In this paper the optimization of type-2 fuzzy inference systems using genetic algorithms (GAs) and particle swarm optimization (PSO) is presented. The optimized type-2 fuzzy inference systems are used to estimate the type-2 fuzzy weights of backpropagation neural networks. Simulation results and a comparative study among neural networks with type-2 fuzzy weights without optimization of the type-2 fuzzy inference systems, neural networks with optimized type-2 fuzzy weights using genetic algorithms, and neural networks with optimized type-2 fuzzy weights using particle swarm optimization are presented to illustrate the advantages of the bio-inspired methods. The comparative study is based on a benchmark case of prediction, which is the Mackey-Glass time series (for ?=17) problem.
['Fernando Gaxiola', 'Patricia Melin', 'Fevrier Valdez', 'Juan R. Castro', 'Oscar Castillo']
Optimization of type-2 fuzzy weights in backpropagation learning for neural networks using GAs and PSO