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Article: On the design of neural-fuzzy control system

TitleOn the design of neural-fuzzy control system
Authors
KeywordsComputer Simulation
Computer Software
Fuzzy Control
Learning Algorithms
Mathematical Models
Membership Functions
Neural Networks
Issue Date1998
PublisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www3.interscience.wiley.com/cgi-bin/jhome/36062
Citation
International Journal of Intelligent Systems, 1998, v. 13 n. 1, p. 11-26 How to Cite?
AbstractThis article presents a neural-network-based fuzzy logic control (NN-FLC) system. The NN-FLC model has the learning capabilities for constructing membership functions and extracting fuzzy rules from training examples. Both unsupervised and supervised training algorithms are used to find the membership functions of the FLC. Competitive learning algorithms are employed to evaluate fuzzy logic rules. Matlab programs using both neural and fuzzy toolboxes are developed to implement the NTST-FLC model. Computer simulations of the inverted pendulum controlled by NN-FLC system were conducted to illustrate the self-learning ability of the network. © 1998 John Wiley & Sons, Inc.
Persistent Identifierhttp://hdl.handle.net/10722/91114
ISSN
2015 Impact Factor: 2.05
2015 SCImago Journal Rankings: 1.361
References

 

DC FieldValueLanguage
dc.contributor.authorKaur, Den_HK
dc.contributor.authorLin, Ben_HK
dc.date.accessioned2010-09-17T10:13:14Z-
dc.date.available2010-09-17T10:13:14Z-
dc.date.issued1998en_HK
dc.identifier.citationInternational Journal of Intelligent Systems, 1998, v. 13 n. 1, p. 11-26en_HK
dc.identifier.issn0884-8173en_HK
dc.identifier.urihttp://hdl.handle.net/10722/91114-
dc.description.abstractThis article presents a neural-network-based fuzzy logic control (NN-FLC) system. The NN-FLC model has the learning capabilities for constructing membership functions and extracting fuzzy rules from training examples. Both unsupervised and supervised training algorithms are used to find the membership functions of the FLC. Competitive learning algorithms are employed to evaluate fuzzy logic rules. Matlab programs using both neural and fuzzy toolboxes are developed to implement the NTST-FLC model. Computer simulations of the inverted pendulum controlled by NN-FLC system were conducted to illustrate the self-learning ability of the network. © 1998 John Wiley & Sons, Inc.en_HK
dc.languageengen_HK
dc.publisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www3.interscience.wiley.com/cgi-bin/jhome/36062en_HK
dc.relation.ispartofInternational Journal of Intelligent Systemsen_HK
dc.subjectComputer Simulationen_HK
dc.subjectComputer Softwareen_HK
dc.subjectFuzzy Controlen_HK
dc.subjectLearning Algorithmsen_HK
dc.subjectMathematical Modelsen_HK
dc.subjectMembership Functionsen_HK
dc.subjectNeural Networksen_HK
dc.titleOn the design of neural-fuzzy control systemen_HK
dc.typeArticleen_HK
dc.identifier.emailLin, B:blin@hku.hken_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-0031699685en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0031699685&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume13en_HK
dc.identifier.issue1en_HK
dc.identifier.spage11en_HK
dc.identifier.epage26en_HK

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