TY - JOUR
T1 - A genetic based fuzzy-neural networks design for system identification
AU - Yen, T. G.
AU - Kang, C. C.
AU - Wang, W. J.
PY - 2005
Y1 - 2005
N2 - In this paper, we use a modified Genetic Algorithm (MGA) to construct a fuzzy neural network (FNN), spontaneously, which can approximate a nonlinear function as well as possible. With the specific structure of the chromosome, the special mutation operation and the adequate fitness function, the proposed method with MGA produces a FNN with minimum structure of neural network, smaller number of rules, suitable placement of the premise's fuzzy sets and proper location of the consequent singletons. Finally, an example is illustrated to show the effectiveness of the proposed method on the nonlinear function approximation.
AB - In this paper, we use a modified Genetic Algorithm (MGA) to construct a fuzzy neural network (FNN), spontaneously, which can approximate a nonlinear function as well as possible. With the specific structure of the chromosome, the special mutation operation and the adequate fitness function, the proposed method with MGA produces a FNN with minimum structure of neural network, smaller number of rules, suitable placement of the premise's fuzzy sets and proper location of the consequent singletons. Finally, an example is illustrated to show the effectiveness of the proposed method on the nonlinear function approximation.
KW - Fuzzy neural network
KW - Genetic algorithms
UR - http://www.scopus.com/inward/record.url?scp=27944452201&partnerID=8YFLogxK
M3 - 會議論文
AN - SCOPUS:27944452201
SN - 1062-922X
VL - 1
SP - 672
EP - 678
JO - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
JF - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
T2 - IEEE Systems, Man and Cybernetics Society, Proceedings - 2005 International Conference on Systems, Man and Cybernetics
Y2 - 10 October 2005 through 12 October 2005
ER -