@article{oai:kanazawa-u.repo.nii.ac.jp:00009841,
 author = {Cao, Qiping and Gao, Shangce and Zhang, Jianchen and Tang, Zheng and Kimura, Haruhiko},
 issue = {5},
 journal = {IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences},
 month = {May},
 note = {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., 金沢大学理工研究域電子情報学系},
 pages = {1085--1092},
 title = {A stochastic dynamic local search method for learning Multiple-Valued Logic networks},
 volume = {E90-A},
 year = {2007}
}