DocumentCode
2244966
Title
Identification of recurrent fuzzy systems with genetic algorithms
Author
Evsukoff, Alexandre G. ; Ebecken, Nelson F F
Author_Institution
COPPE, Fed. Univ. of Rio de Janeiro, Brazil
Volume
3
fYear
2004
fDate
25-29 July 2004
Firstpage
1703
Abstract
This work presents an algorithm for identification of fuzzy recurrent models of non-linear dynamic systems. The identification algorithm is based on a general purpose genetic algorithm. The resulting recurrent fuzzy system can encode into a fuzzy finite state automaton in which the linguistic terms of the fuzzy model are the states, and rule base weights are transition possibilities. The identification algorithm is tested against benchmark identification problems found in the literature.
Keywords
fuzzy systems; genetic algorithms; identification; nonlinear systems; genetic algorithm; identification algorithm; nonlinear dynamic system; recurrent fuzzy system; Automata; Benchmark testing; Encoding; Fuzzy neural networks; Fuzzy systems; Genetic algorithms; Input variables; Neural networks; Nonlinear dynamical systems; Parameter estimation;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 2004. Proceedings. 2004 IEEE International Conference on
ISSN
1098-7584
Print_ISBN
0-7803-8353-2
Type
conf
DOI
10.1109/FUZZY.2004.1375437
Filename
1375437
Link To Document