滤波辨识(12):多变量OEARMA系统的滤波辅助模型递阶广义增广迭代参数辨识
Filtering Identification.Part L:Filtering-Based Auxiliary Model Hierarchical Generalized Extended Iterative Parameter Identification for Multivariable OEARMA Systems
丁锋 1万立娟 2栾小丽 3徐玲 3刘喜梅2
作者信息
- 1. 江南大学 物联网工程学院,江苏 无锡 214122;青岛科技大学 自动化与电子工程学院,山东 青岛 266061
- 2. 青岛科技大学 自动化与电子工程学院,山东 青岛 266061
- 3. 江南大学 物联网工程学院,江苏 无锡 214122
- 折叠
摘要
针对多变量输出误差自回归滑动平均(M-OEARMA)系统,即多变量Box-Jenkins系统,利用滤波辨识理念和辅助模型辨识思想,研究和提出了滤波辅助模型递阶广义增广梯度迭代辨识方法、滤波辅助模型递阶多新息广义增广梯度迭代辨识方法、滤波辅助模型递阶递推广义增广最小二乘迭代辨识方法、滤波辅助模型递阶多新息广义增广最小二乘迭代辨识方法等.这些滤波辅助模型递阶广义增广迭代辨识方法可以推广到其它有色噪声干扰下的线性和非线性多变量随机系统中.
Abstract
For multivariable output-error autoregressive moving average(M-OEARMA)models,which are also called multivariable Box-Jenkins(M-BJ)models,this paper investi-gates and proposes filtered auxiliary-model hierarchical generalized extended gradient-based iterative identification methods,filtered auxiliary-model hierarchical multi-innovation gener-alized extended gradient-based iterative identification methods,filtered auxiliary-model hier-archical generalized extended least squares-based iterative identification methods,and filtered auxiliary-model hierarchical multi-innovation generalized extended least squares-based itera-tive identification methods by using the filtering identification idea and the auxiliary-model i-dentification idea from available input-output data.These filtered auxiliary-model hierarchi-cal generalized extended iterative identification methods can be extended to other linear and nonlinear multivariable stochastic systems with colored noises.
关键词
参数估计/迭代辨识/多新息辨识/递阶辨识/滤波辨识/最小二乘/多变量系统Key words
parameter estimation/iterative identification/multi-innovation identification/hi-erarchical identification/filtering identification/least squares/multivariable system引用本文复制引用
出版年
2024
NETL
NSTL
万方数据