摘要
针对一阶Ｔａｋａｇｉ－Ｓｕｇｅｎｏ模型辨识复杂系统的困难，提出一种新的模糊模型．这种模型的结构在一阶Ｔａｋａｇｉ－Ｓｕｇｅｎｏ模型的基础上，再进行一次非线性映射．文中运用卡尔曼滤波算法的模糊神经元网络实现了这种模型．仿真结果表明该方法辨识精度高。
In this paper ,a new fuzzy model is presented to overcome the difficulty of using the first order Takagi Sugeno model to identify complex systems. The structure of the new model is based on the first order Takagi Sugeno model, but a nonlinear mapping is added to. In order to realize the model, a fuzzy neural network(FNN) with Kalman filter algorithm is then implemented. Simulation results show that this method is very efficient and practical.
出处
《自动化学报》
EI
CSCD
北大核心
1997年第6期817-821,共5页
Acta Automatica Sinica
关键词
模糊辨识
神经元网络
系统辨识
复杂系统
Fuzzy identificatioin, fuzzy neural network, system identification.
