DocumentCode
2271666
Title
Dynamic modelling of a paper making process based on bilinear system modelling and genetic neural networks
Author
Borairi, M. ; Wang, H. ; Roberts, J.C.
Author_Institution
Dept. of Paper Sci., Univ. of Manchester Inst. of Sci. & Technol., UK
fYear
1998
fDate
1-4 Sep 1998
Firstpage
1277
Abstract
The dynamic modelling of the wet end of the paper machines has been recognised as a challenging problem due to its nonlinear, complex, time-varying, time-delayed, and multivariable interactive properties. This paper presents a methodology based on bilinear system modelling and multilayer perceptron (MLP) neural network for modelling of such a complex system. Genetic algorithm (GA) search and optimisation technique is proposed to train the neural network weights. This logical combination has advantages of both physical and genetic neural modellings
Keywords
paper industry; GA search; MLP neural network; bilinear system modelling; dynamic modelling; genetic algorithm search; genetic neural networks; multilayer perceptron neural network; neural network weight training; nonlinear complex time-varying time-delayed multivariable interactive properties; optimisation; paper making process; wet end;
fLanguage
English
Publisher
iet
Conference_Titel
Control '98. UKACC International Conference on (Conf. Publ. No. 455)
Conference_Location
Swansea
ISSN
0537-9989
Print_ISBN
0-85296-708-X
Type
conf
DOI
10.1049/cp:19980411
Filename
726103
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