TY - GEN
T1 - Design of high performance fuzzy controllers using flexible parameterized membership functions and intelligent genetic algorithms
AU - Ho, Shinn-Ying
AU - Chen, Tai Kang
AU - Ho, Shinn Jang
PY - 2002
Y1 - 2002
N2 - This paper proposes a method for designing high performance fuzzy controllers with a compact rule system. The method is mainly derived from flexible parameterized membership functions (FPMFs) and a novel intelligent genetic algorithm (IGA). Each FPMF consists of flexible trapezoidal fuzzy sets and the fuzzy set is encoded by five parameters. Furthermore, the membership functions and fuzzy rules are simultaneously determined by effectively incorporating all the system parameters into chromosomes. Therefore, the optimal design of fuzzy controllers is formulated as a large parameter optimization problem, which can be effectively solved by IGA. The proposed method is demonstrated by two well-known problems, truck backing and cart centering problems. It is shown empirically that the performance of the proposed method is superior to those of existing methods in terms of the numbers of time steps and fuzzy rules.
AB - This paper proposes a method for designing high performance fuzzy controllers with a compact rule system. The method is mainly derived from flexible parameterized membership functions (FPMFs) and a novel intelligent genetic algorithm (IGA). Each FPMF consists of flexible trapezoidal fuzzy sets and the fuzzy set is encoded by five parameters. Furthermore, the membership functions and fuzzy rules are simultaneously determined by effectively incorporating all the system parameters into chromosomes. Therefore, the optimal design of fuzzy controllers is formulated as a large parameter optimization problem, which can be effectively solved by IGA. The proposed method is demonstrated by two well-known problems, truck backing and cart centering problems. It is shown empirically that the performance of the proposed method is superior to those of existing methods in terms of the numbers of time steps and fuzzy rules.
UR - http://www.scopus.com/inward/record.url?scp=84901418001&partnerID=8YFLogxK
U2 - 10.1109/CEC.2002.1004444
DO - 10.1109/CEC.2002.1004444
M3 - Conference contribution
AN - SCOPUS:84901418001
SN - 0780372824
SN - 9780780372825
T3 - Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002
SP - 1378
EP - 1383
BT - Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002
PB - IEEE Computer Society
T2 - 2002 Congress on Evolutionary Computation, CEC 2002
Y2 - 12 May 2002 through 17 May 2002
ER -