量化Wiener系统的重复梯度学习参数估计  

Repetitive gradient learning parameter estimation of quantized Wiener system

作　　者：曹昊哲 周金笨 李丽华[1] CAO Hao-zhe;ZHOU Jin-ben;LI Li-hua(School of National Security,People’s Public Security University of China,Beijing 100038,China)

机构地区：[1]中国人民公安大学国家安全学院,北京100038

出　　处：《吉林大学学报(工学版)》2024年第12期3673-3680,共8页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家重点研发计划项目(31400);中国人民公安大学基本科研业务费学科基础理论体系项目(2022JKF02019)。

摘　　要：为解决Wiener系统量化辨识问题,提出了一种重复学习梯度辨识算法。首先,基于分解技术构建量化Wiener系统参数分离的辨识模型,减少算法计算量。然后,利用数据窗理论对观测数据向量进行延拓,以获取更多的量化系统模态。为了解决窗长度抑制辨识性能的问题,将重复学习思想引入参数自适应律更新机制中,将窗数据批次更新转化为多次标量更新,提高估计性能。最后,估计器收敛性分析和实例对比结果证明了本文方案的有效性和优势。In order to address the identification of the quantized Wiener system,a repetitive gradient learning identification algorithm was proposed.Firstly,based on the decomposition technique,the quantified Wiener system was transformed into an identification model with parameter separation,in which the computational burden of was reduced.Secondly,the observation data were extended using data window theory to obtain more quantitative system modal information.To address the issue of moving time window length,the idea of repetitive learning was integrated into the parameter adaptive law update mechanism,which greatly improves the estimation performance.Finally,the convergence of the estimator and the comparisons of example have used to show the effectiveness and advantages of the proposed algorithm.

关 键 词：系统工程 系统辨识 维纳系统 梯度估计 重复学习 量化观测 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]