Modular Echo State Neural Networks in Time Series Prediction

Abstract

Echo State neural networks (ESN), which are a special case of recurrent neural networks, are studied from the viewpoint of their learning ability, with a goal to achieve their greater predictive ability. In this paper we study the influence of the memory length on predictive abilities of Echo State neural networks. The conclusion is that Echo State neural networks with fixed memory length can have troubles with adaptation of its intrinsic dynamics to dynamics of the prediction task. Therefore, we have tried to create complex prediction system as a combination of the local expert Echo State neural networks with different memory length and one special gating Echo State neural network. This approach was tested in laser fluctuations and turbojet gas temperature prediction. The prediction error achieved by this approach was substantially smaller in comparison with prediction error achieved by standard Echo State neural networks.