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Article: On the design of neural-fuzzy control system
Title | On the design of neural-fuzzy control system |
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Authors | |
Keywords | Computer Simulation Computer Software Fuzzy Control Learning Algorithms Mathematical Models Membership Functions Neural Networks |
Issue Date | 1998 |
Publisher | John 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? |
Abstract | This 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 Identifier | http://hdl.handle.net/10722/91114 |
ISSN | 2023 Impact Factor: 5.0 2023 SCImago Journal Rankings: 1.264 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
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dc.contributor.author | Kaur, D | en_HK |
dc.contributor.author | Lin, B | en_HK |
dc.date.accessioned | 2010-09-17T10:13:14Z | - |
dc.date.available | 2010-09-17T10:13:14Z | - |
dc.date.issued | 1998 | en_HK |
dc.identifier.citation | International Journal of Intelligent Systems, 1998, v. 13 n. 1, p. 11-26 | en_HK |
dc.identifier.issn | 0884-8173 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/91114 | - |
dc.description.abstract | This 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.language | eng | en_HK |
dc.publisher | John Wiley & Sons, Inc. The Journal's web site is located at http://www3.interscience.wiley.com/cgi-bin/jhome/36062 | en_HK |
dc.relation.ispartof | International Journal of Intelligent Systems | en_HK |
dc.subject | Computer Simulation | en_HK |
dc.subject | Computer Software | en_HK |
dc.subject | Fuzzy Control | en_HK |
dc.subject | Learning Algorithms | en_HK |
dc.subject | Mathematical Models | en_HK |
dc.subject | Membership Functions | en_HK |
dc.subject | Neural Networks | en_HK |
dc.title | On the design of neural-fuzzy control system | en_HK |
dc.type | Article | en_HK |
dc.identifier.email | Lin, B:blin@hku.hk | en_HK |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.scopus | eid_2-s2.0-0031699685 | en_HK |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-0031699685&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 13 | en_HK |
dc.identifier.issue | 1 | en_HK |
dc.identifier.spage | 11 | en_HK |
dc.identifier.epage | 26 | en_HK |
dc.identifier.isi | WOS:000071305300002 | - |
dc.identifier.issnl | 0884-8173 | - |