摘要
研究了输出误差(OE)系统和输出误差自回归滑动平均(OEARMA)系统(即Box-Jenkins系统)的辅助模型随机梯度算法、辅助模型多新息随机梯度算法、辅助模型递推最小二乘算法、辅助模型多新息最小二乘算法,输出误差系统的修正辅助模型随机梯度算法、遗忘因子辅助模型随机梯度算法、变递推间隔辅助模型随机梯度算法、变递推间隔辅助模型多新息随机梯度算法、变递推间隔辅助模型递推最小二乘算法等,以及输出误差自回归(OEAR)系统的基于滤波的辅助模型多新息广义随机梯度算法和基于滤波的辅助模型多新息广义最小二乘算法.
This paper studies the auxiliary model based stochastic gradient ( AM?SG ) algorithm, the auxiliary model based multi?innovation stochastic gradient ( AM?MISG) algorithm,the auxiliary model based recursive least squares ( AM?RLS) algorithm and the auxiliary model based multi?innovation least squares algorithm for output?error systems and output?error autoregressive moving average ( OEARMA) systems ( namely,Box?Jenkins systems) , the modified AM?SG algorithm,the forgetting factor AM?SG algorithm,the interval?varying AM?SG algorithm,the in?terval?varying AM?MISG algorithm and the interval?varying AM?RLS algorithm for output?error systems,and presents the filtering based auxiliary model generalized stochastic gradient algorithm and the filtering based multi?innovation generalized least squares algorithm for output?error autoregressive systems ( namely,OEAR systems) .
出处
《南京信息工程大学学报（自然科学版）》
CAS
2015年第6期481-503,共23页
Journal of Nanjing University of Information Science & Technology（Natural Science Edition）
基金
国家自然科学基金(61273194)
江苏省自然科学基金(BK2012549)
高等学校学科创新引智"111计划"(B12018)
关键词
参数估计
递推辨识
梯度搜索
最小二乘
滤波
分解
辅助模型辨识思想
多新息辨识理论
递阶辨识原理
输出误差系统
线性系统
parameter estimation
recursive identification
gradient search
least squares
filtering
decomposition
auxiliary model identification idea
multi-innovation identification theory
hierarchical identification principle
output-error system
linear system
