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	Convergence and robustness of bounded recurrent neural networks for solving dynamic Lyapunov equations
	Wang, Guancheng1,2; Hao, Zhihao1; Zhang, Bob1; Jin, Long3
	2022-04-01
摘要	Recurrent neural networks have been reported as an effective approach to solve dynamic Lyapunov equations, which widely exist in various application fields. Considering that a bounded activation function should be imposed on recurrent neural networks to solve the dynamic Lyapunov equation in certain situations, a novel bounded recurrent neural network is defined in this paper. Following the definition, several bounded activation func-tions are proposed, and two of them are used to construct the bounded recurrent neural network for demonstration, where one activation function has a finite-time convergence property and the other achieves robustness against noise. Moreover, theoretical analyses provide rigorous and detailed proof of these superior properties. Finally, extensive simula-tion results, including comparative numerical simulations and two application examples, are demonstrated to verify the effectiveness and feasibility of the proposed bounded recur-rent neural network.(c) 2021 Elsevier Inc. All rights reserved.
关键词	Recurrent neural network dynamic Lyapunov equations Bounded activation functions Finite-time convergence Robustness
DOI	10.1016/j.ins.2021.12.039
发表期刊	INFORMATION SCIENCES
ISSN	0020-0255
卷号	588页码:106-123
通讯作者	Zhang, Bob(bobzhang@um.edu.mo) ; Jin, Long(jinlongsysu@foxmail.com)
收录类别	SCI
WOS记录号	WOS:000768300300006
语种	英语