基于遗传模拟退火的广义T-S模糊模型的混沌系统辨识  

Chaotic system identification based on adaptable T-S fuzzy model and algorithms of GA-annealing strategy

作　　者：张静[1] 

机构地区：[1]襄樊学院物理系,湖北襄樊441000

出　　处：《计算机应用》2005年第11期2671-2672,2675,共3页journal of Computer Applications

基　　金：湖北省教育厅科研项目(2001D69001)

摘　　要：针对混沌系统辨识引入广义T-S模糊模型,使系统中隶属函数具有自适应性;并对T-S模糊模型前件模糊规则数、各加权值、隶属函数自适应参数进行遗传退火算法优化,使系统具有最佳结构和参数。以一维的Logistic系统和二维的Henon系统为例进行仿真分析,结果表明辨识模型能够拟合原混沌系统,收敛速度及精度良好。The adaptable T-S fuzzy model with adaptable membership functions was proposed to identify chaotic system. The structure and parameters of this model were optimized by the algorithms of GA-Annealing strategy. The simulations to identify chaotic systems of Logistic system and Henon system show that the identified models can approach the original systems and have rapid convergence rate and good precision.

关 键 词：混沌系统 辨识 广义T—S模糊模型 遗传退火算法 

分 类 号：TP273.4[自动化与计算机技术—检测技术与自动化装置]