Merging echo state and feedforward neural networks for time series forecasting

  • Authors:

  • Štefan Babinec;Jiří Pospíchal

  • Affiliations:

  • Department of Mathematics, Fac. of Chemical and Food Technologies, Slovak University of Technology, Bratislava, Slovakia;Institute of Applied Informatics, Fac. of Informatics and Information Technologies, Slovak University of Technology, Bratislava, Slovakia

  • Venue:
  • ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
  • Year:
  • 2006

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Abstract

Echo state neural networks, 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 prediction ability. A standard training of these neural networks uses pseudoinverse matrix for one-step learning of weights from hidden to output neurons. Such learning was substituted by backpropagation of error learning algorithm and output neurons were replaced by feedforward neural network. This approach was tested in temperature forecasting, and the prediction error was substantially smaller in comparison with the prediction error achieved either by a standard echo state neural network, or by a standard multi-layered perceptron with backpropagation.