Performance analysis of least squares algorithm for multivariable stochastic systems

IF 0.9 4区计算机科学 Q4 COMPUTER SCIENCE, CYBERNETICS

Kybernetika Pub Date : 2023-03-10 DOI:10.14736/kyb-2023-1-0028

Ziming Wang, Yiming Xing, Xinghua Zhu

引用次数: 0

Abstract

In this paper, we consider the parameter estimation problem for the multivariable system. A recursive least squares algorithm is studied by minimizing the accumulative prediction error. By employing the stochastic Lyapunov function and the martingale estimate methods, we provide the weakest possible data conditions for convergence analysis. The upper bound of accumulative regret is also provided. Various simulation examples are given, and the results demonstrate that the convergence rate of the algorithm depends on the parameter dimension and output dimension.

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多变量随机系统最小二乘算法的性能分析

本文研究多变量系统的参数估计问题。研究了一种最小化累计预测误差的递推最小二乘算法。利用随机Lyapunov函数和鞅估计方法，给出了收敛性分析的最弱数据条件。并给出了累计后悔的上界。仿真结果表明，该算法的收敛速度与参数维数和输出维数有关。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Kybernetika 工程技术-计算机：控制论

CiteScore

1.30

自引率

20.00%

发文量

审稿时长

6 months

期刊介绍： Kybernetika is the bi-monthly international journal dedicated for rapid publication of high-quality, peer-reviewed research articles in fields covered by its title. The journal is published by Nakladatelství Academia, Centre of Administration and Operations of the Czech Academy of Sciences for the Institute of Information Theory and Automation of The Czech Academy of Sciences. Kybernetika traditionally publishes research results in the fields of Control Sciences, Information Sciences, Statistical Decision Making, Applied Probability Theory, Random Processes, Operations Research, Fuzziness and Uncertainty Theories, as well as in the topics closely related to the above fields.