Summary
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An Approach to Collaboration of Growing Self-Organizing Maps and Adaptive Resonance Theory Maps Masaru TAKANASHI Hiroyuki TORIKAI Toshimichi SAITO Publication IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences Vol.E90-A No.9 pp.2047-2050 Publication Date: 2007/09/01 Online ISSN: 1745-1337 DOI: 10.1093/ietfec/e90-a.9.2047 Print ISSN: 0916-8508 Type of Manuscript: LETTER Category: Neural Networks and Bioengineering Keyword: self-organizing maps, adaptive resonance theory, combinatorial optimization, Full Text: PDF(243.6KB)>>
Summary: Collaboration of growing self-organizing maps (GSOM) and adaptive resonance theory maps (ART) is considered through traveling sales-person problems (TSP).The ART is used to parallelize the GSOM: it divides the input space of city positions into subspaces automatically. One GSOM is allocated to each subspace and grows following the input data. After all the GSOMs grow sufficiently they are connected and we obtain a tour. Basic experimental results suggest that we can find semi-optimal solution much faster than serial methods. | ||||||||||
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