基于智能优化算法的T-S模糊模型辨识  

T-S fuzzy model identification based on intelligent optimization algorithms

作　　者：刘福才[1] 窦金梅[1] 王树恩[1] 

机构地区：[1]燕山大学工业计算机控制工程河北省重点实验室,河北秦皇岛066004

出　　处：《系统工程与电子技术》2013年第12期2643-2650,共8页Systems Engineering and Electronics

基　　金：河北省自然科学基金(F2010001320)资助课题

摘　　要：将智能算法应用在T-S模糊模型的辨识方面,是模糊系统辨识的一种新途径。文中对几种智能优化算法,如遗传算法(genetic algorithm,GA)、粒子群(particle swarm optimization,PSO)算法、菌群优化(bacterial foraging optimization,BFO)算法等的优化原理和在模糊辨识方面的应用现状进行了综述和分析,并给出了它们在T-S模糊模型辨识中对参数进行优化的过程。最后将这些优化方法用于一非线性动态系统的建模,并对仿真结果进行了对比和详细的分析,为进一步了解这几种优化方法在模糊模型辨识参数优化方面的作用提供了仿真实验依据。It is a new way apply intelligent algorithms to the identification of T-S fuzzy systems. This paper gives a detail description of several intelligent optimization algorithms and their application of fuzzy identifica-tion, such as genetic algorithm （GA）, particle swarm optimization （PSO） algorithm and bacterial foraging opti-mization （BFO） algorithm. The parameters optimization processes of these algorithms on the T-S fuzzy model are also provided. Finally, the described methods are applied to the modeling of a nonlinear dynamic system, and the simulation results are analyzed iri detail. This paper provides an opportunity for a further understanding of the effects of these methods on the process of parameters optimization in fuzzy modeling.

关 键 词：T—S模型辨识 群智能算法 遗传算法 菌群优化算法 粒子群算法 

分 类 号：TP273.4[自动化与计算机技术—检测技术与自动化装置]