非线性系统辨识中模糊模型参数收敛问题的进一步研究

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Further Study on the Parameter Convergence of Fuzzy Models in Nonlinear System Identifications

1.
Dept. of EEE, FST, University of Macau, Taipa, Macau SAR, P. R. China 2. National Laboratory of Industrial Control Technology, Institute of Modern Control Engineering, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, P. R. China

摘要: 对于使用标准的Mamdani 型模糊系统及正交投影参数调整算法进行非线性系统辨识，基于模糊模型参数的估计值收敛到其真实值所需的持续激励条件，给出了适用于非线性移动平均模型和二阶非线性自回归移动平均模型系统辨识的持续激励输入信号设计的几个算法.
- 非线性系统辨识 /
- 模糊系统模型 /
- 参数收敛性 /
- 持续激励输入信号设计
Abstract: This paper investigates the persistent excitation conditions under which the parameters in the fuzzy system model converge to their true values when the standard Mamdani type fuzzy system is constructed and the orthogonal projection parameter-tuning algorithm is used for nonlinear system identification. Algorithms are proposed accordingly for generating the input signals with persistent excitation property for the identifications of nonlinear moving average (N-MA) and second-order nonlinear auto-regressive moving average (N-ARMA) systems.
- Nonlinear system identification /
- fuzzy system model /
- parameter convergence /
- persistently exciting input signal design