基于复值稀疏Bayesian的系统稳定性辨识  

System Stability Identification Based on Complex-Valued Sparse Bayesian

作　　者：谢伟翔 陈安琪 XIE Weixiang;CHEN Anqi(School of Mathematics&Statistics,Shaoguan University,Shaoguan 512005,Guangdong,China)

机构地区：[1]韶关学院数学与统计学院,广东韶关512005

出　　处：《韶关学院学报》2024年第6期14-20,共7页Journal of Shaoguan University

基　　金：韶关学院校级自然科学类科研项目(SY2021KJ11)。

摘　　要：稀疏Bayesian学习是近年来机器学习研究的热点,基于Szeg?核的复值稀疏Bayesian学习算法能提供稀疏的有理逼近.提出基于Szeg?核的复值稀疏Bayesian学习算法来判定单位圆盘内闭环系统的稳定性,该方法具有可给出逼近的解析表达式和适用范围更广的优点,并且不需要参数控制进行迭代优化,运算速度快.实验结果表明,此方法是有效的.Sparse Bayesian learning is a hot topic in machine learning research in recent years,and the complex-valued sparse Bayesian learning algorithm based on the Szegökernel can provide sparse rational approximation.A complex-valued sparse Bayesian learning algorithm based on the Szegö̈kernel is proposed to determine the stability of the closed-loop system in the unit disk.This method has the advantages of giving approximate analytical expressions and a wider range of applications,and does not require parameter control for iteration optimized for fast operation.Experimental results show that this method is effective.

关 键 词：稀疏Bayesian 稳定系统 Szeg?核 稳定性判据 

分 类 号：O242.2[理学—计算数学]