Combination of dynamic reservoir and feedforward neural network for time series forecasting

Abstract

Echo State neural networks are a special case of recurrent neural networks. The most important part of Echo State neural networks is so called "dynamic reservoir". Echo State neural networks use dynamics of this massive and randomly initialized dynamic reservoir to extract interesting properties of incoming sequences. A standard training of these neural networks uses pseudo inverse matrix for one-step learning of weights from hidden to output neurons. In this approach, we have merged this dynamic reservoir with standard feedforward neural network, with a goal to achieve greater prediction ability. This approach was tested in laser fluctuations and Mackey-Glass time series prediction. The prediction error achieved by this approach was substantially smaller in comparison with prediction error achieved by standard algorithm or time delay neural network with backpropagation algorithm. © 2009 Springer Berlin Heidelberg.