Optimization of type-2 fuzzy weight for neural network using genetic algorithm and particle swarm optimization

Abstract

In this paper two bio-inspired methods are applied to optimize the type-2 fuzzy inference systems used in the neural network with type-2 fuzzy weights. The genetic algorithm and particle swarm optimization are used to optimize the two type-2 fuzzy systems that work in the backpropagation learning method with type-2 fuzzy weight adjustment. The mathematical analysis of the learning method architecture and the adaptation of type-2 fuzzy weights are presented. In this work an optimized type-2 fuzzy inference systems to manage weights for the neural network and the results for the two bio-inspired methods are presented. The proposed approach is applied to a case of time series prediction, specifically in Mackey-Glass time series.