Extended self organizing feature map: A tagged potential field approach

Date

1999-8

Author

Baykal, Nazife

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This paper proposes an escape methodology to the local minima problem of self organizing feature maps generated in the overlapping regions which are equidistant to the corresponding winners. Two new versions of the Self Organizing Feature Map are derived equipped with such a methodology. The first approach introduces an excitation term, which increases the convergence speed and efficiency of the algorithm, while increasing the probability of escaping from local minima. In the second approach, we associate a learning set which specifies the attractive and repulsive field of output neurons. Results indicate that accuracy percentile of the new methods are higher than the original algorithm while they have the ability to escape from local minima.

Subject Keywords

Self Organizing Feature Map, Neural Modeling, Local Minima Avoidance, Classification, Radial Basis Function Networks

URI

https://hdl.handle.net/11511/52496

Journal

NEURAL PROCESSING LETTERS

DOI

https://doi.org/10.1023/A:1018662813803

Collections

Graduate School of Informatics, Article

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Citation Formats

N. Baykal, “Extended self organizing feature map: A tagged potential field approach,” NEURAL PROCESSING LETTERS, pp. 57–72, 1999, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52496.