CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
A stochastic dynamic local search method for learning Multiple-Valued Logic networks
Authors
Cao Qiping
Gao Shangce
+3 more
Kimura Haruhiko
Tang Zheng
Zhang Jianchen
Publication date
1 May 2007
Publisher
'Oxford University Press (OUP)'
Doi
Cite
Abstract
金沢大学理工研究域電子情報学系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
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
Kanazawa University Repository for Academic Resources
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:kanazawa-u.repo.nii.ac.jp:...
Last time updated on 06/05/2019