TY - GEN
T1 - A T-S model identification method based on harmony search algorithm
AU - Huang, Xianlin
AU - Song, Qingnan
AU - Ban, Xiaojun
AU - Gao, Xiaozhi
PY - 2010
Y1 - 2010
N2 - The conventional T-S fuzzy model identification methods, such as the fuzzy c-means (FCM) algorithm and least-squares method, usually fail to find the optimal solutions, because they determine the consequent parameters based on only one certain group of the premise parameters. That is to say, these techniques are usually trapped into the local optima in the multidimensional parameter space. In the present paper, a new hybrid identification algorithm(HIA) is introduced to overcome the above drawback. Our method can simultaneously optimize the premise and consequent parameters by merging the harmony search algorithm(HS), FCM algorithm and least-squares method together. This hybrid approach also has the remarkable feature of error feedback mechanism. Simulation results demonstrate that the proposed optimization algorithm can effectively escape from the local optima, and yield a superior performance over the regular parameter identification methods.
AB - The conventional T-S fuzzy model identification methods, such as the fuzzy c-means (FCM) algorithm and least-squares method, usually fail to find the optimal solutions, because they determine the consequent parameters based on only one certain group of the premise parameters. That is to say, these techniques are usually trapped into the local optima in the multidimensional parameter space. In the present paper, a new hybrid identification algorithm(HIA) is introduced to overcome the above drawback. Our method can simultaneously optimize the premise and consequent parameters by merging the harmony search algorithm(HS), FCM algorithm and least-squares method together. This hybrid approach also has the remarkable feature of error feedback mechanism. Simulation results demonstrate that the proposed optimization algorithm can effectively escape from the local optima, and yield a superior performance over the regular parameter identification methods.
KW - Error feedback mechanism
KW - Harmony search (HS)
KW - Hybrid identification algorithm(HIA)
KW - Local optima
KW - T-S model identification
UR - https://www.scopus.com/pages/publications/78650253581
M3 - 会议稿件
AN - SCOPUS:78650253581
SN - 9787894631046
T3 - Proceedings of the 29th Chinese Control Conference, CCC'10
SP - 1224
EP - 1229
BT - Proceedings of the 29th Chinese Control Conference, CCC'10
T2 - 29th Chinese Control Conference, CCC'10
Y2 - 29 July 2010 through 31 July 2010
ER -