A Local Search Based Learning Method for Multiple-Valued Logic Networks(Neural Networks and Bioengineering)
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概要
- 論文の詳細を見る
This paper describes a new learning method for Multiple-Value Logic (MVL) networks using the local search method. It is a "non-back-propagation" learning method which constructs a layered MVL network based on canonical realization of MVL functions, defines an error measure between the actual output value and teacher's value and updates a randomly selected parameter of the MVL network if and only if the updating results in a decrease of the error measure. The learning capability of the MVL network is confirmed by simulations on a large number of 2-variable 4-valued problems and 2-variable 16-valued problems. The simulation results show that the method performs satisfactorily and exhibits good properties for those relatively small problems.
- 社団法人電子情報通信学会の論文
- 2003-07-01
著者
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Wang Xu-gang
Faculty Of Engineering Toyama University
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TANG Zheng
Faculty of Engineering, Toyama University
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CAO Qi-Ping
Tateyama Systems Institute
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WANG Rong-Long
Faculty of Engineering, Fukui University
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Wang Rong-long
Faculty Of Engineering Toyama University
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Tang Zheng
Faculty Of Engineering Miyazaki University
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Wang Rong-long
Faculty Of Engineering Fukui University
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