Control of a pH neutralization process using a modified Elman neural net

Author

Kwok, D.P. ; Tam, P. ; Zhou, K.

Author_Institution

Dept. of Electr. Eng., Hong Kong Polytech., Kowloon, Hong Kong

fYear

1994

fDate

29 Nov-2 Dec 1994

Firstpage

71

Lastpage

75

Abstract

A modified Elman neural network is utilized to construct control systems for industrial processes. The basic structure of a modified Elman network is introduced. A specific 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 proposed 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. Numerical results for the control of a pH neutralization process are also presented

Keywords

chemical technology; control systems; feedback; feedforward neural nets; learning (artificial intelligence); neurocontrollers; pH control; process control; recurrent neural nets; control systems; feedback format; feedforward connections; feedforward format; identification system; industrial processes; inverse model; modified Elman neural net; pH neutralization process control; parallel pattern; partially recurrent neural networks; self-feedback connections; specific learning algorithm; Control system synthesis; Control systems; Electrical equipment industry; Electronics industry; Industrial control; Industrial electronics; Neural networks; Neurofeedback; Process control; Recurrent neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information Systems,1994. Proceedings of the 1994 Second Australian and New Zealand Conference on

Conference_Location

Brisbane, Qld.

Print_ISBN

0-7803-2404-8

Type

conf

DOI

10.1109/ANZIIS.1994.396947

Filename

396947