Modular neural networks for time series prediction using type-1 fuzzy logic integration

Abstract

In this paper, a new method to perform the times series prediction using modular neural networks with type-1 fuzzy logic integration is proposed. The proposed method consists in the division of a dataset into modules and each module learns a specific period of the time series. Each module has a prediction, but the final prediction is obtained using type-1 fuzzy logic. To prove the effectiveness of the proposed method, 800 points of the Mackey-Glass time series are used; 500 points are used for the training phase and 300 for the testing phase. In this work the number of modules is fixed established but the number of points in each module for the training phase is randomly established. Different trainings are performed using different modular neural architectures (number of hidden layers and neurons) and a type-1 fuzzy integrator is used to perform a final comparison.