基于混合协同粒子群优化的广义T-S模糊模型训练方法

Abstract

Abstract: To solve the high-dimensional,nonlinearity,mixed-parameter optimization problem during training generalized Takagi-Sugeno fuzzy model(GTSFM),a method for training GTSFM is proposed using hybrid cooperative particle swarm optimization.The structural parameters of models are denoted by the position of discrete binary particles,and the parameters of the membership function in the model rule are denoted by the position of ordinary particles.The combination of positions of the two kinds of particles constitutes a complete premise parameters set of a model.All reasoning parameters are adjusted by cooperative coevolution of two particle swarms;all consequent parameters are estimated by Kalman filtering algorithm.The method does not request any previous information about objects and is able to produce a compact fuzzy model with the better properties of generalization.The numerical simulation of function approximation shows the validity of the method.

CLC Number:

TP273

ZHOU Xin-ran, TENG Zhao-sheng, YI Zhao. Training method for generalized Takagi-Sugeno fuzzy model by hybrid cooperative particle swarm optimization[J]. Journal of Systems Engineering and Electronics, 2009, 31(5): 1189-1193.

References

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Training method for generalized Takagi-Sugeno fuzzy model by hybrid cooperative particle swarm optimization

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