【摘要】 针对类多变量输出误差自回归滑动平均(M-OEARMA-like)系统，利用辅助模型辨识思想，滤波辨识理念和递阶辨识原理，研究和提出了滤波辅助模型递阶广义增广随机梯度辨识方法、滤波辅助模型递阶多新息广义增广随机梯度辨识方法、滤波辅助模型递阶广义增广递推梯度辨识方法、滤波辅助模型递阶多新息广义增广递推梯度辨识方法、滤波辅助模型递阶递推广义增广最小二乘辨识方法、滤波辅助模型递阶多新息广义增广最小二乘辨识方法。这些滤波辅助模型递阶广义增广参数辨识方法可以推广到其他有色噪声干扰下的线性和非线性多变量随机系统中。更多还原

【Abstract】 For multivariable output-error autoregressive moving average-like(M-OEARMA-like) models, this paper investigates and proposes filtered auxiliary model hierarchical generalized extended stochastic gradient identification method, filtered auxiliary model hierarchical multi-innovation generalized extended stochastic gradient identification method, filtered auxiliary model hierarchical generalized extended recursive gradient identification method, filtered auxiliary model hierarchical multi-innovation generalized extended recursive gradient identification method, filtered auxiliary model hierarchical generalized extended least squares identification method, and filtered auxiliary model hierarchical multi-innovation generalized extended least squares identification method by using the auxiliary model identification idea, the filtering identification idea and the hierarchical identification principle from available input-output data. These filtered auxiliary model hierarchical generalized extended parameter identification methods can be extended to other linear and nonlinear multivariable stochastic systems with colored noises.更多还原