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	Novel Discrete-Time Recurrent Neural Networks Handling Discrete-Form Time-Variant Multi-Augmented Sylvester Matrix Problems and Manipulator Application
	Shi, Yang1; Jin, Long2; Li, Shuai3; Li, Jian4; Qiang, Jipeng1; Gerontitis, Dimitrios K.5
	2022-02-01
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.
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
DOI	10.1109/TNNLS.2020.3028136
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
卷号	33期号:2页码:587-599
通讯作者	Shi, Yang(shiy@yzu.edu.cn) ; Jin, Long(jinlongsysu@foxmail.com)
收录类别	SCI
WOS记录号	WOS:000752016400014
语种	英语