Abstract
New results for online prediction using predictive networks composed of smaller prediction units are presented. Strategies for choosing training signals across a range of signal types are discussed. Composite networks are shown to generalise across a wide range of test signals. The best network found by a genetic evolution is present, simplified, and analysed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Floreano, D., Dürr, P., Mattiussi, C.: Neuroevolution: from architectures to learning. Evolutionary Intelligence 1, 47–62 (2008)
Schrauwen, B., Verstraeten, D., Van Campenhout, J.: An overview of reservoir computing: theory, applications and implementations. In: Proceedings of the 15th European Symposium on Artificial Neural Networks, pp. 471–482 (2007)
Webb, R.Y.: Time series prediction with evolved, composite echo state networks. In: Xiaodong (ed.) SEAL 2008. LNCS, vol. 5361, pp. 555–564. Springer, Heidelberg (2008)
Jaeger, H., Haas, H.: Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science 304(5667), 78–80 (2004)
Webb, Russell: Multi-layer corrective cascade architecture for on-line predictive echo state networks. Applied Artificial Intelligence 22(7), 811–823 (2008)
Gao, H., Sollacher, R., Kriegel, H.P.: Spiral recurrent neural network for online learning
Fette, G., Eggert, J.: Short term memory and pattern matching with simple echo state networks. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3696, pp. 13–18. Springer, Heidelberg (2005)
Cernansky, M., Makula, M.: Feed-forward echo state networks. In: IJCNN 2005. Proceedings. 2005 IEEE International Joint Conference on Neural Networks, vol. 3, pp. 1479–1482 (2005)
Mackey, M.C., Glass, L.: Oscillation and chaos in physiological control systems. Science 197(4300), 287–289 (1977)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Webb, R.Y. (2009). Training Methods and Analysis of Composite, Evolved, On-Line Networks for Time Series Prediction. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_147
Download citation
DOI: https://doi.org/10.1007/978-3-642-02478-8_147
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02477-1
Online ISBN: 978-3-642-02478-8
eBook Packages: Computer ScienceComputer Science (R0)