辨识方法的计算效率(2):迭代算法  

Computational efficiency of the identification methods. Part B: Iterative algorithms

作　　者：丁锋[1,2,3] 

机构地区：[1]江南大学物联网工程学院,无锡214122 [2]江南大学控制科学与工程研究中心,无锡214122 [3]江南大学教育部轻工过程先进控制重点实验室,无锡214122

出　　处：《南京信息工程大学学报（自然科学版）》2012年第5期385-401,共17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计划(B12018)

摘　　要：讨论了最小二乘迭代辨识算法及其计算效率问题.最小二乘迭代算法由于涉及矩阵求逆运算,为减小计算量,提出了基于块矩阵求逆的最小二乘迭代辨识算法.基于块矩阵求逆的最小二乘迭代辨识算法不是一种新算法,只是从辨识算法的实现方式上降低计算负担,它与最小二乘迭代算法产生相同的参数估计,但计算量小.文中研究了伪线性回归系统、多元伪线性回归系统、多变量伪线性回归系统的最小二乘迭代辨识算法及其基于块矩阵求逆的最小二乘迭代算法.This paper focuses on the computational efficiency of the least squares based iterative algorithms. The computational burdens of the least squares based iterative （LSI） algorithms are heavy due to computing large-size matrix inversion. In order to reduce the computational burdens, the block matrix inversion based LSI algorithms are presented. The proposed methods can reduce the computational cost through simplifying the implementation of the least squares based iterative algorithms, thus the estimation accuracies remain unchanged. The least squares based iterative algorithms and the block matrix inversion based LSI methods are studied for pseudo-linear regression systems, multivariate pseudo-linear regression systems and muhivariahle pseudo-linear systems.

关 键 词：递推辨识 迭代辨识 参数估计 FIR模型 方程误差模型 CAR模型 CARMA模型 CARAR模型 CARARMA模型 输出误差模型 OEMA模型 OEAR模型 辅助模型辨识 多新息辨识 递阶辨识 耦合辨识 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]