Further Study on the Parameter Convergence of Fuzzy Models in Nonlinear System Identifications
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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.
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关键词:
- 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. -
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