A Stochastic Dynamic Local Search Method for Learning Multiple-Valued Logic Networks

Qiping CAO
Shangce GAO
Jianchen ZHANG
Zheng TANG
Haruhiko KIMURA

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E90-A    No.5    pp.1085-1092
Publication Date: 2007/05/01
Online ISSN: 1745-1337
DOI: 10.1093/ietfec/e90-a.5.1085
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Neural Networks and Bioengineering
Multiple-Valued Logic,  local search,  stochastic dynamic,  temporary,  iteration,  

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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.