Optimization of ensemble neural networks with fuzzy integration using the particle swarm algorithm for time series prediction

Abstract

This paper describes an optimization method based on the particle swarm algorithm for designing ensemble neural networks with fuzzy response aggregation to forecast complex time series. The time series that was considered in this paper, to compare the hybrid approach with traditional methods, is the Mackey Glass benchmark time series. Simulation results are presented for the optimization of the structure of the ensemble neural network with type-1 and type-2 fuzzy response integration and its optimization with genetic algorithms. The Simulation results show that the ensemble approach produces good prediction of the Mackey Glass time series.