Abstract
In this paper, we introduce the Gamma SOM model for temporal sequence processing. The standard SOM is merged with a new context descriptor based on a short term memory structure called Gamma memory. The proposed model allows increasing depth without losing resolution, by adding more contexts. When using a single stage of the Gamma filter, the Merge SOM model is recovered. The temporal quantization error is used as a performance measure. Simulation results are presented using two data sets: Mackey-Glass time series, and Bicup 2006 challenge time series. Gamma SOM surpassed Merge SOM in terms of lower temporal quantization error in these data sets.
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
Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1995)
Strickert, M., Hammer, B.: Merge SOM for Temporal Data. Neurocomputing 64, 39–72 (2005)
Voegtlin, T.: Recursive Self-Organizing Maps. Neural Networks 15, 979–991 (2002)
Hammer, B., Micheli, A., Neubauer, N., Sperduti, A., Strickert, M.: Self Organizing Maps for Time Series. In: Proceedings of the Workshop on Self-Organizing Maps (WSOM), Paris, pp. 115–122 (2005)
Hammer, B., Micheli, A., Sperduti, A., Strickert, M.: Recursive Self-Organizing Network Models. Neural Networks 17, 1061–1085 (2004)
Chappell, G.J., Taylor, J.G.: The Temporal Kohönen Map. Neural Networks 6, 441–445 (1993)
De Vries, B., Principe, J.C.: The Gamma model- A New Neural Model for Temporal Processing. Neural Networks 5, 565–576 (1992)
Principe, J.C., Giuliano, N.R., Lefebvre, W.C.: Neural and Adaptive Systems. John Wiley & Sons, Inc., New York (1999)
Principe, J.C., Kuo, J.M., Celebi, S.: An Analysis of the Gamma Memory in Dynamic Neural Networks. IEEE Trans. on Neural Networks 5, 331–337 (1994)
Principe, J.C., De Vries, B., De Oliveira, P.G.: The Gamma Filter – A New Class of Adaptive IIR Filters with Restricted Feedback. IEEE Transactions on Signal Processing 41, 649–656 (1993)
Tiño, P., Farkas, I., van Mourik, J.: Dynamics and Topographic Organization of Recursive Self-Organizing Maps. Neural Computation 18, 2529–2567 (2006)
Martinetz, T.M., Bercovich, S.G., Schulten, K.J.: “Neural-gas” Network for Vector Quantization and its Application to Time-Series Prediction. IEEE Transactions on Neural Networks 4, 558–569 (1993)
Strickert, M., Hammer, B.: Neural Gas for Sequences. In: Yamakawa, T. (ed.) Proceedings of the Workshop on Self-Organizing Networks (WSOM), Kyushu, Japan, pp. 53–58 (2003)
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
Estévez, P.A., Hernández, R. (2009). Gamma SOM for Temporal Sequence Processing. In: Príncipe, J.C., Miikkulainen, R. (eds) Advances in Self-Organizing Maps. WSOM 2009. Lecture Notes in Computer Science, vol 5629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02397-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-02397-2_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02396-5
Online ISBN: 978-3-642-02397-2
eBook Packages: Computer ScienceComputer Science (R0)