Title :
Process identification using a modified Elman neural net
Author :
Kwok, D.P. ; Wang, P. ; Zhou, K.
Author_Institution :
Dept. of Electron. Eng., Hong Kong Polytech., Kowloon, Hong Kong
Abstract :
In this paper, a modified Elman neural network (1990) is utilized to identify industrial processes. The basic structure of the Elman network is introduced and one of its modified versions is presented. 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. Numerical results for the identification of a pH neutralization process are also included
Keywords :
feedforward neural nets; identification; recurrent neural nets; feedforward connections; modified Elman neural net; pH neutralization process; partially recurrent neural networks; process identification; self-feedback connections; Control systems; Electrical equipment industry; Electronics industry; Industrial control; Industrial electronics; Neural networks; Neurofeedback; Real time systems; Recurrent neural networks; System identification;
Conference_Titel :
Speech, Image Processing and Neural Networks, 1994. Proceedings, ISSIPNN '94., 1994 International Symposium on
Print_ISBN :
0-7803-1865-X
DOI :
10.1109/SIPNN.1994.344784
