Design of interval type-2 fuzzy neural networks and their optimization using real-coded genetic algorithms

Abstract

In this paper, we introduce the design methodology of interval type-2 fuzzy neural networks (IT2FNN). And to optimize the network we use a real-coded genetic algorithm. IT2FNN is the network of combination between the fuzzy neural network (FNN) and interval type-2 fuzzy set with uncertainty. The antecedent part of the network is composed of the fuzzy division of input space and the consequence part of the network is represented by polynomial functions. The parameters such as the apexes of membership function, uncertainty parameter, the learning rate and the momentum coefficient are optimized using genetic algorithm (GA). The proposed network is evaluated with the performance between the approximation and the generalization abilities.