Optimization of Type-2 and Type-1 Fuzzy Integrator to Ensemble Neural Network with Fuzzy Weights Adjustment

Abstract

In this paper, two bio-inspired methods are applied to optimize the type-2 and type-1 fuzzy integrator used in the neural network with fuzzy weights. The genetic algorithm and particle swarm optimization are used to optimize the type-2 and type-1 fuzzy integrator that work in the integration of the output for the ensemble neural network with three networks. One neural network uses type-2 fuzzy inference systems with Gaussian membership functions to obtain the fuzzy weights; the second neural network uses type-2 fuzzy inference systems with triangular membership functions; and the third neural network uses type-2 fuzzy inference systems with triangular membership functions with uncertainty in the standard deviation. In this work, an optimized type-2 and type-1 fuzzy integrator to manage the output of the ensemble 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.