Particle Swarm Optimization of the Fuzzy Integrators for Time Series Prediction Using Ensemble of IT2FNN Architectures

Abstract

This paper describes the construction of intelligent hybrid architectures and the optimization of the fuzzy integrators for time series prediction; interval type-2 fuzzy neural networks (IT2FNN). IT2FNN used hybrid learning algorithm techniques (gradient descent backpropagation and gradient descent with adaptive learning rate backpropagation). The IT2FNN is represented by Takagi–Sugeno–Kang reasoning. Therefore this TSK IT2FNN is represented as an adaptive neural network with hybrid learning in order to automatically generate an interval type-2 fuzzy logic system (TSK IT2FLS). We use interval type-2 and type-1 fuzzy systems to integrate the output (forecast) of each Ensemble of ANFIS models. Particle Swarm Optimization (PSO) was used for the optimization of membership functions (MFs) parameters of the fuzzy integrators. The Mackey-Glass time series is used to test of performance of the proposed architecture. Simulation results show the effectiveness of the proposed approach.