Abstract
A framework for predictive, on-line, learning networks composed of multiple echo state networks is presented. These composite networks permit learning predictions based on complex combinations of sub-predictions and error terms. The configuration space is explored with a genetic algorithm and better performance is achieved than with hand coded solutions.
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© 2008 Springer-Verlag Berlin Heidelberg
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Webb, R.Y. (2008). Time Series Prediction with Evolved, Composite Echo State Networks. In: Li, X., et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_56
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DOI: https://doi.org/10.1007/978-3-540-89694-4_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89693-7
Online ISBN: 978-3-540-89694-4
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