Optimization of ensemble neural networks with type-2 fuzzy response integration for predicting the Mackey-Glass time series

This paper describes the optimization of an ensemble neural network with fuzzy integration of responses based on type-1 and type-2 fuzzy logic. Genetic algorithms are used as a method of optimization for the ensemble model in this case of study. The time series that is being considered is the Mackey-Glass benchmark. Simulation results show that the ensemble approach produces good prediction of the Mackey-Glass time series.