A New Varying-Factor Finite-Time Recurrent Neural Network to Solve the Time-Varying Sylvester Equation Online

Haoming Tan, Junyun Wu (), Hongjie Guan, Zhijun Zhang, Ling Tao, Qingmin Zhao and Chunquan Li
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Haoming Tan: School of Electric and Electronic Enginnering, Shanghai University of Engineering Science, Shanghai 201620, China
Junyun Wu: School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China
Hongjie Guan: School of Information Engineering, Nanchang University, Nanchang 330031, China
Zhijun Zhang: School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
Ling Tao: Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction, Nanchang 330031, China
Qingmin Zhao: Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction, Nanchang 330031, China
Chunquan Li: Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction, Nanchang 330031, China

Mathematics, 2024, vol. 12, issue 24, 1-22

Abstract: This paper presents a varying-parameter finite-time recurrent neural network, called a varying-factor finite-time recurrent neural network (VFFTRNN), which is able to solve the solution of the time-varying Sylvester equation online. The proposed neural network makes the matrix coefficients vary with time and can achieve convergence in a finite time. Apart from this, the performance of the network is better than traditional networks in terms of robustness. It is theoretically proved that the proposed neural network has super-exponential convergence performance. Simulation results demonstrate that this neural network has faster convergence speed and better robustness than the return to zero neural networks and can track the theoretical solution of the time-varying Sylvester equation effectively.

Keywords: recurrent neural network (RNN); finite time; super-exponential convergence rate (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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