Training the self-organizing feature map using hybrids of genetic and Kohonen methods

M. McInerney, Atam Dhawan

Research output: Contribution to conferencePaperpeer-review

8 Scopus citations

Abstract

The self-organizing feature map is expected to produce a topologically correct mapping between input and output spaces. This mapping is usually found with the Kohonen learning rule which is sensitive to its parameter values. A poor choice of parameters results in a mapping that may not be topologically correct. In this paper we describe a hybrid algorithm of genetic methods with Kohonen learning that avoids this problem. Experimental results show that this algorithm always results in a topologically correct mapping.

Original languageEnglish (US)
Pages641-644
Number of pages4
StatePublished - Dec 1 1994
Externally publishedYes
EventProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
Duration: Jun 27 1994Jun 29 1994

Other

OtherProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CityOrlando, FL, USA
Period6/27/946/29/94

All Science Journal Classification (ASJC) codes

  • Software

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