Presentation 1994/6/17
Binary Neural Network with Negative Self-Feedback and Its Application
Masaya Ohta, Akio Ogihara, Kunio Fukunaga,
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Abstract(in English) Authors nave proposed the binary neural network with negative self-feedback connections which has the special selection rule for every state change.In this article,this model is applied to Traveling Salesman Problem(TSP)and is improved to get an optimum solution.First,we explain that the exact adjustment of the penalty coefficient of TSP is not needed for the model and that negative self-feedback connections are estimated from the penalty coefficient easily.Secondly,its selection rule is improved.By the improvement,the probability that we success to get an optimum solution in TSP of 10 cities is 100 %.Furthermore,we propose a coupling of the two networks which are running with parallel and better solution is obtained than the original network.
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Keyword(in English) Neural Network / Hopfield′s Model / Negative Self-Feedback Conne ction / TSP
Paper # NLP94-29
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Conference Information
Committee NLP
Conference Date 1994/6/17(1days)
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Paper Information
Registration To Nonlinear Problems (NLP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Binary Neural Network with Negative Self-Feedback and Its Application
Sub Title (in English)
Keyword(1) Neural Network
Keyword(2) Hopfield′s Model
Keyword(3) Negative Self-Feedback Conne ction
Keyword(4) TSP
1st Author's Name Masaya Ohta
1st Author's Affiliation Department of Electrical Engineering,College of Engineering, University of Osaka Prefecture()
2nd Author's Name Akio Ogihara
2nd Author's Affiliation Department of Computer and Systems Sciences,College of Engineering, University of Osaka Prefecture
3rd Author's Name Kunio Fukunaga
3rd Author's Affiliation Department of Computer and Systems Sciences,College of Engineering, University of Osaka Prefecture
Date 1994/6/17
Paper # NLP94-29
Volume (vol) vol.94
Number (no) 98
Page pp.pp.-
#Pages 8
Date of Issue