DocumentCode
3222757
Title
A new method for fuzzy models identification
Author
Cipriano, A. ; Ramos, M. ; Montoya, F.
Author_Institution
Fac. of Eng., Catholic Univ. of Chile, Santiago, Chile
Volume
2
fYear
1995
fDate
6-10 Nov 1995
Firstpage
1514
Abstract
In this paper a new method for fuzzy models identification is presented. The algorithm is based on an iterative procedure and determines the optimal membership functions through the minimization of the root mean square error. Using illustrative examples the new method is evaluated and compared with the identification algorithm of Sugeno and Yasukawa, and the Horikawa algorithm
Keywords
fuzzy set theory; identification; iterative methods; Horikawa algorithm; Sugeno and Yasukawa algorithm; fuzzy models identification; identification algorithm; iterative procedure; optimal membership functions; root mean square error minimisation; Automatic control; Backpropagation algorithms; Clustering algorithms; Fuzzy sets; Input variables; Iterative algorithms; Least squares methods; Minimization methods; Neural networks; Parameter estimation;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial Electronics, Control, and Instrumentation, 1995., Proceedings of the 1995 IEEE IECON 21st International Conference on
Conference_Location
Orlando, FL
Print_ISBN
0-7803-3026-9
Type
conf
DOI
10.1109/IECON.1995.484175
Filename
484175
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