A stochastic dynamic local search method for learning Multiple-Valued Logic networks
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概要
- 論文の詳細を見る
In this paper, we propose a stochastic dynamic local search (SDLS) method for Multiple-Valued Logic (MVL) learning by introducing stochastic dynamics into the traditional local search method. The proposed learning network maintains some trends of quick descent to either global minimum or a local minimum, and at the same time has some chance of escaping from local minima by permitting temporary error increases during learning. Thus the network may eventually reach the global minimum state or its best approximation with very high probability. Simulation results show that the proposed algorithm has the superior abilities to find the global minimum for the MVL network learning within reasonable number of iterations. Copyright © 2007 The Institute of Electronics, Information and Communication Engineers.
- 社団法人電子情報通信学会の論文
- 2007-05-01
著者
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TANG Zheng
Faculty of Engineering, Toyama University
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Tang Zheng
Univ. Toyama Toyama‐shi Jpn
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Tang Zheng
Faculty Of Engineering Miyazaki University
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GAO Shangce
Graduate School of Innovative Life Science, University of Toyama
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CAO Qiping
Tateyama Institute of System
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TANG Zheng
Graduate School of Innovative Life Science, University of Toyama
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Tang Zheng
Univ. Of Toyama Toyama‐shi Jpn
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Tang Zheng
Graduate School Of Innovative Life Science University Of Toyama
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Gao Shangce
Faculty Of Engineering University Of Toyama
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ZHANG Jianchen
Hiscom Co. Ltd.
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ZHANG Jianchen
Faculty of Engineering, University of Toyama
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KIMURA Haruhiko
Graduate School of Natural Science, Kanazawa University
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Kimura Haruhiko
Graduate School Of Natural Science Kanazawa University
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Tang Zheng
Department Of Computer Science And Technology The Key Laboratory Of Embedded System And Service Comp
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