期刊
COMPUTERS & MATHEMATICS WITH APPLICATIONS
卷 59, 期 8, 页码 2615-2627出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.camwa.2010.01.030
关键词
Recursive identification; Parameter estimation; Multivariable systems; Stochastic gradient; Convergence properties
资金
- National Natural Science Foundation of China
This paper studies the convergence of the stochastic gradient identification algorithm of multi-input multi-output ARX-like systems (i.e., multivariable ARX-like systems) by using the stochastic martingale theory. This ARX-like model contains a characteristic polynomial and differs from the conventional multivariable ARX system. The results indicate that the parameter estimation errors converge to zero under the persistent excitation conditions. The simulation results validate the proposed convergence theorem. (C) 2010 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据