Abstract
The self-organizing map (SOM) method developed by Kohonen is a powerful tool for visualizing high-dimensional datasets. To improve the adaptability of SOM, this paper proposes a new model based on Kohenen SOM. The new model has integrated a series of evolutionary working mechanisms of neurons. The microcosmic analysis gives the reason that those introduced mechanisms can conquer the problems of instability in competitive learning. The empirical evidences show the performance of the proposed model.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Berlin (2001)
Kraaijveld, M.A., Mao, J., Jain, A.K.: A Nonlinear Projection Method Based an Kohonen’s Topology Preserving Maps. IEEE Trans. Neural Networks 6, 548–559 (1995)
Rubner, J., Schlten, P.: Development of Feature Detectors by Self-Organization. Biol. Cybern. 62, 193–199 (1990)
Kohonen, T., Oja, E., Simula, O., Visa, A., Kangas, J.: Engineering Application of the Self-Organizing Map. Proc. IEEE. 84(10), 1358–1383 (1996)
Su, M.C., Chang, H.T.: A New Model of Self-Organizing Neural Networks and Its Application in Data Projection. IEEE Trans. Neural Networks 12(1), 153–158 (2001)
Murtagh, F.: Interpreting the Kohonen Self-Organizing Feature Map Using Contiguity Constrained Clustering. Pattern Recognition Lett. 16, 399–408 (1995)
Merkl, D., Rauber, A.: Alternative Ways for Cluster Visualization in Self- Organizing Maps. In: Proc. Workshop on Self-Organizing Maps, pp. 106–111 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Weng, S., Wong, F., Zhang, C. (2004). A New Adaptive Self-Organizing Map. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_35
Download citation
DOI: https://doi.org/10.1007/978-3-540-28647-9_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
eBook Packages: Springer Book Archive