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Novel Discrete-Time Recurrent Neural Networks Handling Discrete-Form Time-Variant Multi-Augmented Sylvester Matrix Problems and Manipulator Application

文献类型：期刊论文

作者Shi, Yang3; Jin, Long5; Li, Shuai2; Li, Jian1; Qiang, Jipeng3; Gerontitis, Dimitrios K.4
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2022-02-01
卷号33期号:2页码:587-599
关键词Mathematical model Linear matrix inequalities Numerical models Computational modeling Recurrent neural networks Analytical models Manipulators Convergence discrete-form time-variant multi-augmented Sylvester matrix problems discrete-time recurrent neural networks (RNNs) discretization formula robot manipulator application
ISSN号2162-237X
DOI10.1109/TNNLS.2020.3028136
通讯作者Shi, Yang(shiy@yzu.edu.cn) ; Jin, Long(jinlongsysu@foxmail.com)
英文摘要In this article, the discrete-form time-variant multi-augmented Sylvester matrix problems, including discrete-form time-variant multi-augmented Sylvester matrix equation (MASME) and discrete-form time-variant multi-augmented Sylvester matrix inequality (MASMI), are formulated first. In order to solve the above-mentioned problems, in continuous time-variant environment, aided with the Kronecker product and vectorization techniques, the multi-augmented Sylvester matrix problems are transformed into simple linear matrix problems, which can be solved by using the proposed discrete-time recurrent neural network (RNN) models. Second, the theoretical analyses and comparisons on the computational performance of the recently developed discretization formulas are presented. Based on these theoretical results, a five-instant discretization formula with superior property is leveraged to establish the corresponding discrete-time RNN (DTRNN) models for solving the discrete-form time-variant MASME and discrete-form time-variant MASMI, respectively. Note that these DTRNN models are zero stable, consistent, and convergent with satisfied precision. Furthermore, illustrative numerical experiments are given to substantiate the excellent performance of the proposed DTRNN models for solving discrete-form time-variant multi-augmented Sylvester matrix problems. In addition, an application of robot manipulator further extends the theoretical research and physical realizability of RNN methods.
资助项目National Natural Science Foundation of China[61906164] ; National Natural Science Foundation of China[61703189] ; National Natural Science Foundation of China[61703362] ; Natural Science Foundation of Jiangsu Province of China[BK20190875] ; Natural Science Foundation of Jiangsu Province of China[BK20170513] ; Natural Science Foundation of Chongqing, China[cstc2020jcyjzdxm0047] ; Light of West China Program, Chinese Academy of Sciences (CAS)
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000752016400014
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.138/handle/2HOD01W0/15257]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Shi, Yang; Jin, Long
作者单位1.Xinyang Normal Univ, Sch Comp & Informat Technol, Xinyang 464000, Peoples R China
2.Hong Kong Polytech Univ, Dept Comp, Hung Hom, Hong Kong, Peoples R China
3.Yangzhou Univ, Sch Informat Engn, Yangzhou 225127, Jiangsu, Peoples R China
4.Aristotle Univ Thessaloniki, Sch Informat, Thessaloniki 54124, Greece
5.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
推荐引用方式
GB/T 7714		 Shi, Yang,Jin, Long,Li, Shuai,et al. Novel Discrete-Time Recurrent Neural Networks Handling Discrete-Form Time-Variant Multi-Augmented Sylvester Matrix Problems and Manipulator Application[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022,33(2):587-599.
APA Shi, Yang,Jin, Long,Li, Shuai,Li, Jian,Qiang, Jipeng,&Gerontitis, Dimitrios K..(2022).Novel Discrete-Time Recurrent Neural Networks Handling Discrete-Form Time-Variant Multi-Augmented Sylvester Matrix Problems and Manipulator Application.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,33(2),587-599.
MLA Shi, Yang,et al."Novel Discrete-Time Recurrent Neural Networks Handling Discrete-Form Time-Variant Multi-Augmented Sylvester Matrix Problems and Manipulator Application".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 33.2(2022):587-599.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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