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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Babinec, Š., Pospíchal, J. (2006). Merging Echo State and Feedforward Neural Networks for Time Series Forecasting. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840817_39
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DOI: https://doi.org/10.1007/11840817_39
Publisher Name: Springer, Berlin, Heidelberg
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