辅助模型辨识方法(6):性能分析

Auxiliary model based identification methods.Part F: Performance Analysis

辨识方法的性能分析是系统辨识领域的重要而困难的研究课题.新的辨识方法一诞生,就伴随着收敛性分析.辅助模型辨识是辨识的一个分支,辅助模型辨识方法已成为一大类辨识方法族,关于其收敛性也提出了许多课题.本文研究了输出误差系统辅助模型随机梯度算法、辅助模型递推最小二乘算法、辅助模型多新息随机梯度算法、变间隔辅助模型随机梯度算法和变间隔辅助模型递推最小二乘算法的一致收敛性,近似分析了Box-Jenkins系统辅助模型递推广义增广最小二乘算法的性能.

Performance analysis of identification methods is the important and difficult projects in the area of system identification. Once one new identification method is born,its convergence analysis appears. The auxiliary model identification is a branch of system identification and has become a large family of identification methods,their convergence brings many projects.This paper studies the consistent convergence of the auxiliary model( AM)based stochastic gradient( SG) algorithm,the AM recursive least squares( RLS) algorithm,the AM multiinnovation SG algorithm,the interval-varying AM SG algorithm and the interval-varying AM RLS algorithm for output-error systems,and analyzes approximately the convergence of the AM recursive generalized extended least squares algorithm for Box-Jenkins systems.