A recurrent neural network based on Taylor difference for solving discrete time-varying linear matrix problems and application in robot arms

IF 3.7 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of The Franklin Institute-engineering and Applied Mathematics Pub Date : 2025-01-01 DOI:10.1016/j.jfranklin.2024.107469

Chenfu Yi, Xuan Li, Mingdong Zhu, Jianliang Ruan

{"title":"A recurrent neural network based on Taylor difference for solving discrete time-varying linear matrix problems and application in robot arms","authors":"Chenfu Yi, Xuan Li, Mingdong Zhu, Jianliang Ruan","doi":"10.1016/j.jfranklin.2024.107469","DOIUrl":null,"url":null,"abstract":"<div><div>Discrete time-varying linear matrix problems (DTVLMP) play an important role in the field of artificial intelligence and control engineering. This article presents a direct solution to the DTVLMP based on Taylor difference discrete time-varying recurrent neural network (TD-DRNN) model. First of all, the TD-DRNN operates in a directly discrete time-varying framework, avoiding the need to build a theoretical foundation from a continuous time-varying recurrent neural network (RNN) model. Then, the theoretical properties of the TD-DRNN have been rigorously analyzed, demonstrating both its convergence and accuracy. These results show that the new TD-DRNN model has remarkable computational performance. Furthermore, the effectiveness and versatility of the TD-DRNN model have been substantiated through a numerical simulation and the application of two robotic trials.</div></div>","PeriodicalId":17283,"journal":{"name":"Journal of The Franklin Institute-engineering and Applied Mathematics","volume":"362 2","pages":"Article 107469"},"PeriodicalIF":3.7000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of The Franklin Institute-engineering and Applied Mathematics","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0016003224008901","RegionNum":3,"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}

引用次数: 0

Abstract

Discrete time-varying linear matrix problems (DTVLMP) play an important role in the field of artificial intelligence and control engineering. This article presents a direct solution to the DTVLMP based on Taylor difference discrete time-varying recurrent neural network (TD-DRNN) model. First of all, the TD-DRNN operates in a directly discrete time-varying framework, avoiding the need to build a theoretical foundation from a continuous time-varying recurrent neural network (RNN) model. Then, the theoretical properties of the TD-DRNN have been rigorously analyzed, demonstrating both its convergence and accuracy. These results show that the new TD-DRNN model has remarkable computational performance. Furthermore, the effectiveness and versatility of the TD-DRNN model have been substantiated through a numerical simulation and the application of two robotic trials.

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of The Franklin Institute-engineering and Applied Mathematics 工程技术-工程：电子与电气

CiteScore

7.30

自引率

14.60%

发文量

586

审稿时长

6.9 months

期刊介绍： The Journal of The Franklin Institute has an established reputation for publishing high-quality papers in the field of engineering and applied mathematics. Its current focus is on control systems, complex networks and dynamic systems, signal processing and communications and their applications. All submitted papers are peer-reviewed. The Journal will publish original research papers and research review papers of substance. Papers and special focus issues are judged upon possible lasting value, which has been and continues to be the strength of the Journal of The Franklin Institute.