Highly efficient maximum-likelihood identification methods for bilinear systems with colored noises

IF 1.4 4区 计算机科学 Q4 AUTOMATION & CONTROL SYSTEMS
Meihang Li, Ximei Liu, Yamin Fan, Feng Ding
{"title":"Highly efficient maximum-likelihood identification methods for bilinear systems with colored noises","authors":"Meihang Li, Ximei Liu, Yamin Fan, Feng Ding","doi":"10.1177/09596518241256145","DOIUrl":null,"url":null,"abstract":"This paper mainly discussed the highly efficient iterative identification methods for bilinear systems with autoregressive moving average noise. Firstly, the input-output representation of the bilinear systems is derived through eliminating the unknown state variables in the model. Then based on the maximum-likelihood principle, a maximum-likelihood gradient-based iterative (ML-GI) algorithm is proposed to identify the parameters of the bilinear systems with colored noises. For improving the computational efficiency, the original identification model is divided into three sub-identification models with smaller dimensions and fewer parameters, and a hierarchical maximum-likelihood gradient-based iterative (H-ML-GI) algorithm is derived by using the hierarchical identification principle. A gradient-based iterative (GI) algorithm is given for comparison. Finally, the algorithms are verified by a simulation example and a practical continuous stirred tank reactor (CSTR) example. The results show that the proposed algorithms are effective for identifying bilinear systems with colored noises and the H-ML-GI algorithm has a higher computational efficiency and a faster convergence rate than the ML-GI algorithm and the GI algorithm.","PeriodicalId":20638,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering","volume":"53 1","pages":""},"PeriodicalIF":1.4000,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1177/09596518241256145","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

This paper mainly discussed the highly efficient iterative identification methods for bilinear systems with autoregressive moving average noise. Firstly, the input-output representation of the bilinear systems is derived through eliminating the unknown state variables in the model. Then based on the maximum-likelihood principle, a maximum-likelihood gradient-based iterative (ML-GI) algorithm is proposed to identify the parameters of the bilinear systems with colored noises. For improving the computational efficiency, the original identification model is divided into three sub-identification models with smaller dimensions and fewer parameters, and a hierarchical maximum-likelihood gradient-based iterative (H-ML-GI) algorithm is derived by using the hierarchical identification principle. A gradient-based iterative (GI) algorithm is given for comparison. Finally, the algorithms are verified by a simulation example and a practical continuous stirred tank reactor (CSTR) example. The results show that the proposed algorithms are effective for identifying bilinear systems with colored noises and the H-ML-GI algorithm has a higher computational efficiency and a faster convergence rate than the ML-GI algorithm and the GI algorithm.
具有彩色噪声的双线性系统的高效最大似然识别方法
本文主要讨论了具有自回归移动平均噪声的双线性系统的高效迭代识别方法。首先,通过消除模型中的未知状态变量,得出双线性系统的输入输出表示。然后,基于最大似然原理,提出了一种基于最大似然梯度的迭代(ML-GI)算法来识别有彩色噪声的双线性系统的参数。为提高计算效率,将原始识别模型划分为三个维度较小、参数较少的子识别模型,并利用分层识别原理推导出分层最大似然梯度迭代(H-ML-GI)算法。此外,还给出了一种基于梯度的迭代算法(GI)供比较。最后,通过一个仿真实例和一个实际的连续搅拌罐反应器(CSTR)实例对算法进行了验证。结果表明,所提出的算法能有效识别具有彩色噪声的双线性系统,而且 H-ML-GI 算法比 ML-GI 算法和 GI 算法具有更高的计算效率和更快的收敛速度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
3.50
自引率
18.80%
发文量
99
审稿时长
4.2 months
期刊介绍: Systems and control studies provide a unifying framework for a wide range of engineering disciplines and industrial applications. The Journal of Systems and Control Engineering refleSystems and control studies provide a unifying framework for a wide range of engineering disciplines and industrial applications. The Journal of Systems and Control Engineering reflects this diversity by giving prominence to experimental application and industrial studies. "It is clear from the feedback we receive that the Journal is now recognised as one of the leaders in its field. We are particularly interested in highlighting experimental applications and industrial studies, but also new theoretical developments which are likely to provide the foundation for future applications. In 2009, we launched a new Series of "Forward Look" papers written by leading researchers and practitioners. These short articles are intended to be provocative and help to set the agenda for future developments. We continue to strive for fast decision times and minimum delays in the production processes." Professor Cliff Burrows - University of Bath, UK This journal is a member of the Committee on Publication Ethics (COPE).cts this diversity by giving prominence to experimental application and industrial studies.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信