{"title":"基于模型的辅助递归最小二乘法和随机梯度算法以及反馈非线性输出误差系统的收敛性分析","authors":"Guangqin Miao, Dan Yang, Feng Ding","doi":"10.1002/acs.3874","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>This paper deals with the problem of the parameter estimation for feedback nonlinear output-error systems. The auxiliary model-based recursive least squares algorithm and the auxiliary model-based stochastic gradient algorithm are derived for parameter estimation. Based on the stochastic process theory, the convergence of the proposed algorithms are proved. The simulation results indicate that the proposed algorithms can estimate the parameters of feedback nonlinear output-error systems effectively.</p>\n </div>","PeriodicalId":50347,"journal":{"name":"International Journal of Adaptive Control and Signal Processing","volume":"38 10","pages":"3268-3289"},"PeriodicalIF":3.9000,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Auxiliary model-based recursive least squares and stochastic gradient algorithms and convergence analysis for feedback nonlinear output-error systems\",\"authors\":\"Guangqin Miao, Dan Yang, Feng Ding\",\"doi\":\"10.1002/acs.3874\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>This paper deals with the problem of the parameter estimation for feedback nonlinear output-error systems. The auxiliary model-based recursive least squares algorithm and the auxiliary model-based stochastic gradient algorithm are derived for parameter estimation. Based on the stochastic process theory, the convergence of the proposed algorithms are proved. The simulation results indicate that the proposed algorithms can estimate the parameters of feedback nonlinear output-error systems effectively.</p>\\n </div>\",\"PeriodicalId\":50347,\"journal\":{\"name\":\"International Journal of Adaptive Control and Signal Processing\",\"volume\":\"38 10\",\"pages\":\"3268-3289\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2024-07-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Adaptive Control and Signal Processing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/acs.3874\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Adaptive Control and Signal Processing","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/acs.3874","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Auxiliary model-based recursive least squares and stochastic gradient algorithms and convergence analysis for feedback nonlinear output-error systems
This paper deals with the problem of the parameter estimation for feedback nonlinear output-error systems. The auxiliary model-based recursive least squares algorithm and the auxiliary model-based stochastic gradient algorithm are derived for parameter estimation. Based on the stochastic process theory, the convergence of the proposed algorithms are proved. The simulation results indicate that the proposed algorithms can estimate the parameters of feedback nonlinear output-error systems effectively.
期刊介绍：
The International Journal of Adaptive Control and Signal Processing is concerned with the design, synthesis and application of estimators or controllers where adaptive features are needed to cope with uncertainties.Papers on signal processing should also have some relevance to adaptive systems. The journal focus is on model based control design approaches rather than heuristic or rule based control design methods. All papers will be expected to include significant novel material.
Both the theory and application of adaptive systems and system identification are areas of interest. Papers on applications can include problems in the implementation of algorithms for real time signal processing and control. The stability, convergence, robustness and numerical aspects of adaptive algorithms are also suitable topics. The related subjects of controller tuning, filtering, networks and switching theory are also of interest. Principal areas to be addressed include:
Auto-Tuning, Self-Tuning and Model Reference Adaptive Controllers
Nonlinear, Robust and Intelligent Adaptive Controllers
Linear and Nonlinear Multivariable System Identification and Estimation
Identification of Linear Parameter Varying, Distributed and Hybrid Systems
Multiple Model Adaptive Control
Adaptive Signal processing Theory and Algorithms
Adaptation in Multi-Agent Systems
Condition Monitoring Systems
Fault Detection and Isolation Methods
Fault Detection and Isolation Methods
Fault-Tolerant Control (system supervision and diagnosis)
Learning Systems and Adaptive Modelling
Real Time Algorithms for Adaptive Signal Processing and Control
Adaptive Signal Processing and Control Applications
Adaptive Cloud Architectures and Networking
Adaptive Mechanisms for Internet of Things
Adaptive Sliding Mode Control.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
