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Implementing Adaptive Fuzzy Logic Controllers with Neural Networks: A Design Paradigm
Fei-Yue Wang; Hung-man Kim
Source PublicationJournal of Intelligent and Fuzzy Systems
1995
Volume3Issue:2Pages:165-180
Abstract
This article proposes a design of adaptive fuzzy logic
based control systems (FLCSs) with neural networks.
A detailed discussion of effects of different reasoning
methods on fuzzy controls is given and used to illustrate
the need for an adaptive implementation of fuzzy
controls. The procedure of decision-making of a FLCS
leads to a neuro-fuzzy network consisting of three types
of subnets for pattern recognition, fuzzy reasoning,
and control synthesis, respectively. The unique knowledge
structure embedded in this structured network
enables it to carry out adaptive changes of fuzzy
reasoning methods and membership functions for both
input signal patterns and output control actions, and
then recover these changes individually and completely
later from its sub nets . Gradient methods for optimization
have been used to derive off-line training rules
and on-line learning algorithms for the structured
neuro-fuzzy network. Issues related to rule modification
and generation for an FLCS are addressed based
on its network implementation. © 1995 John Wiley and
Sons, Inc.
KeywordNeural Networks Design Paradigm
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14166
Collection09年以前成果
Corresponding AuthorFei-Yue Wang
Recommended Citation
GB/T 7714
Fei-Yue Wang,Hung-man Kim. Implementing Adaptive Fuzzy Logic Controllers with Neural Networks: A Design Paradigm[J]. Journal of Intelligent and Fuzzy Systems,1995,3(2):165-180.
APA Fei-Yue Wang,&Hung-man Kim.(1995).Implementing Adaptive Fuzzy Logic Controllers with Neural Networks: A Design Paradigm.Journal of Intelligent and Fuzzy Systems,3(2),165-180.
MLA Fei-Yue Wang,et al."Implementing Adaptive Fuzzy Logic Controllers with Neural Networks: A Design Paradigm".Journal of Intelligent and Fuzzy Systems 3.2(1995):165-180.
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