標題: Water bath temperature control with a neural fuzzy inference network
作者: Lin, CT
Juang, CF
Li, CP
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: neural fuzzy network;backpropagation network;TSK fuzzy rules;recursive least square;structure/parameter learning;similarity measure;water bath temperature control
公開日期: 16-四月-2000
摘要: 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.
URI: http://dx.doi.org/10.1016/S0165-0114(98)00075-X
http://hdl.handle.net/11536/30576
ISSN: 0165-0114
DOI: 10.1016/S0165-0114(98)00075-X
期刊: FUZZY SETS AND SYSTEMS
Volume: 111
Issue: 2
起始頁: 285
結束頁: 306
顯示於類別:期刊論文


文件中的檔案:

  1. 000085432000012.pdf