A New Approach for Chaotic Time Series Prediction Using Recurrent Neural Network

Abstract

A self-constructing fuzzy neural network (SCFNN) has been successfully used for chaotic time series prediction in the literature. In this paper, we propose the strategy of adding a recurrent path in each node of the hidden layer of SCFNN, resulting in a self-constructing recurrent fuzzy neural network (SCRFNN). This novel network does not increase complexity in fuzzy inference or learning process. Specifically, the structure learning is based on partition of the input space, and the parameter learning is based on the supervised gradient descent method using a delta adaptation law. This novel network can also be applied for chaotic time series prediction including Logistic and Henon time series. More significantly, it features rapider convergence and higher prediction accuracy.