DocumentCode
3266308
Title
Fuzzy neural networks in nonlinear system identification
Author
Gexin, Ma ; Dali, Zhang ; Yanda, Li
Author_Institution
Dept. of Autom., Tsinghua Univ., Beijing, China
fYear
1996
fDate
2-6 Dec 1996
Firstpage
375
Lastpage
379
Abstract
There is much interest in closed-loop system identification recently. In this paper, based on historical input output data, we construct initial system model using fuzzy method in order to solve the identifiability of closed-loop systems. The initial model is then modified based on present time data using OLS learning algorithm in order to enhance the precision. The new identification method is used in real data from an ammonia process with satisfactory results
Keywords
closed loop systems; fuzzy neural nets; identification; learning (artificial intelligence); least squares approximations; nonlinear systems; I/O data; OLS learning algorithm; ammonia process; closed-loop system identification; fuzzy neural networks; historical input-output data; initial system model; least squares method; nonlinear system identification; Automation; Closed loop systems; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Intelligent networks; Neural networks; Nonlinear systems; Predictive models; Safety;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Technology, 1996. (ICIT '96), Proceedings of The IEEE International Conference on
Conference_Location
Shanghai
Print_ISBN
0-7803-3104-4
Type
conf
DOI
10.1109/ICIT.1996.601612
Filename
601612
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