Interval type-2 fuzzy weight adjustment for backpropagation neural networks with application in time series prediction

Abstract

In this paper a new backpropagation learning method enhanced with type-2 fuzzy logic is presented. Simulation results and a comparative study among monolithic neural networks, neural network with type-1 fuzzy weights and neural network with type-2 fuzzy weights are presented to illustrate the advantages of the proposed method. In this work, type-2 fuzzy inference systems are used to obtain the type-2 fuzzy weights by applying a different size of the footprint of uncertainty (FOU). The proposed approach is based on recent methods that handle adaptation of weights using fuzzy logic of type-1 and type-2. The proposed approach is applied to a case of prediction for the Mackey-Glass time series (for τ=17). Noise was applied in different levels to the test data of the Mackey-Glass time series for showing that the type-2 fuzzy backpropagation approach obtains better behavior and tolerance to noise than the other methods.