Water bath temperature control with a neural fuzzy inference network

Although multilayered backpropagation neural networks (BPNN) have demonstra ted high potential in the nonconventional branch of adaptive control, its l ong training time usually discourages their applications in industry. Moreo ver, when they are trained on-line to adapt to plant variations, the overtu ned phenomenon usually occurs. To overcome the weakness of the BPNN, we pro pose a neural fuzzy inference network (NFIN) in this paper suitable for ada ptive control of practical plant systems in general, and for adaptive tempe rature 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 neu ral fuzzy networks, where the rules should be decided in advance before par ameter 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 app lied to a water bath temperature control system. As compared to the BPNN un der the same training procedure, the control results show that not only can the NFIN greatly reduce the training time and avoid the overtuned phenomen on, but the NFIN also has perfect regulation ability. (C) 2000 Elsevier Sci ence B.V. All rights reserved.