An upgraded Elman neural network with extra learning capability is utilized to construct process controllers for industrial processes. The basic structure of this upgraded Elman network is introduced. A comprehensive learning algorithm is developed which optimizes not only the feedforward but also the self-feedback connections of such partially recurrent neural networks. The identification system used in the controller design is arranged in a parallel pattern. The control system is devised in a feedforward plus feedback format based on the inverse model identified of the process under control. Numerical results for the control of a pH neutralization process are also presented.
|Number of pages||4|
|State||Published - 1994|
|Event||Joint Conference on Information Sciences - Proceedings, Abstracts and Summaries '94 - Pinehurst, NC, United States|
Duration: 1 Nov 1994 → 1 Nov 1994
|Conference||Joint Conference on Information Sciences - Proceedings, Abstracts and Summaries '94|
|Period||1/11/94 → 1/11/94|