Least Squares Learning Identification

被引：0

作者：

Sun Mingxuan ^{[1
]}

Bi Hongbo ^{[1
]}

机构：

[1] Zhejiang Univ Technol, Coll Informat Engn, Hangzhou 310023, Zhejiang, Peoples R China

来源：

2011 30TH CHINESE CONTROL CONFERENCE (CCC) | 2011年

关键词：

Learning identification; Least squares; Stochastic time-varying systems; CONSISTENCY; ALGORITHMS; PREDICTION; SYSTEMS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents learning identification method for a class of stochastic systems with time-varying parametric uncertainties. The least squares learning algorithm is derived on the basis of repetitive operations over a pre-specified finite time interval. The repetitive persistent excitation is shown to be a sufficient condition for establishing convergence of the learning algorithm. It is shown that the estimates converge to the time-varying values of the parameters over the entire interval, and the complete estimation is achieved. The effectiveness of the proposed learning algorithm is demonstrated by the given numerical results.

引用

页码：1615 / 1620

页数：6

共 17 条

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