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Titolo:
Water bath temperature control with a neural fuzzy inference network
Autore:
Lin, CT; Juang, CF; Li, CP;
Indirizzi:
Natl Chiao Tung Univ, Dept Elect & Control Engn, Hsinchu, Taiwan Natl Chiao Tung Univ Hsinchu Taiwan ect & Control Engn, Hsinchu, Taiwan
Titolo Testata:
FUZZY SETS AND SYSTEMS
fascicolo: 2, volume: 111, anno: 2000,
pagine: 285 - 306
SICI:
0165-0114(20000416)111:2<285:WBTCWA>2.0.ZU;2-0
Fonte:
ISI
Lingua:
ENG
Soggetto:
SYSTEM; LOGIC; IDENTIFICATION;
Keywords:
neural fuzzy network; backpropagation network; TSK fuzzy rules; recursive least square; structure/parameter learning; similarity measure; water bath temperature control;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
32
Recensione:
Indirizzi per estratti:
Indirizzo: Lin, CT Natl Chiao Tung Univ, Dept Elect & Control Engn, Hsinchu, Taiwan Natl Chiao Tung Univ Hsinchu Taiwan ntrol Engn, Hsinchu, Taiwan
Citazione:
C.T. Lin et al., "Water bath temperature control with a neural fuzzy inference network", FUZ SET SYS, 111(2), 2000, pp. 285-306

Abstract

Although multilayered backpropagation neural networks (BPNN) have demonstrated high potential in the nonconventional branch of adaptive control, its long training time usually discourages their applications in industry. Moreover, when they are trained on-line to adapt to plant variations, the overtuned phenomenon usually occurs. To overcome the weakness of the BPNN, we propose a neural fuzzy inference network (NFIN) in this paper suitable for adaptive control of practical plant systems in general, and for adaptive temperature control of a water bath system in particular. The NFIN is inherently a modified TSK (Takagi-Sugeno-Kang)-type fuzzy rule-based model possessing neural network's learning ability. In contrast to the general adaptive neural fuzzy networks, where the rules should be decided in advance before parameter learning is performed, there are no rules initially in the NFIN. The rules in the NFIN are created and adapted as on-line learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a water bath temperature control system. As compared to the BPNN under the same training procedure, the control results show that not only can the NFIN greatly reduce the training time and avoid the overtuned phenomenon, but the NFIN also has perfect regulation ability. (C) 2000 Elsevier Science B.V. All rights reserved.

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Documento generato il 08/04/20 alle ore 09:11:06