Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75012
Title: Nonlinear recurrent neural networks for finite-time solution of general time-varying linear matrix equations
Authors: Xiao, L
Liao, B
Li, S 
Chen, K
Keywords: Finite-time convergence
General time-varying linear matrix equations
Nonlinear activation functions
Nonlinear recurrent neural networks
Issue Date: 2018
Publisher: Elsevier Ltd
Source: Neural networks, 2018, v. 98, p. 102-113 How to cite?
Journal: Neural networks 
Abstract: In order to solve general time-varying linear matrix equations (LMEs) more efficiently, this paper proposes two nonlinear recurrent neural networks based on two nonlinear activation functions. According to Lyapunov theory, such two nonlinear recurrent neural networks are proved to be convergent within finite-time. Besides, by solving differential equation, the upper bounds of the finite convergence time are determined analytically. Compared with existing recurrent neural networks, the proposed two nonlinear recurrent neural networks have a better convergence property (i.e., the upper bound is lower), and thus the accurate solutions of general time-varying LMEs can be obtained with less time. At last, various different situations have been considered by setting different coefficient matrices of general time-varying LMEs and a great variety of computer simulations (including the application to robot manipulators) have been conducted to validate the better finite-time convergence of the proposed two nonlinear recurrent neural networks.
URI: http://hdl.handle.net/10397/75012
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2017.11.011
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