A New Varying-Factor Finite-Time Recurrent Neural Network to Solve the Time-Varying Sylvester Equation Online
Year: 2024
Type: article
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.
Source: Mathematics
DOI: 10.3390/math12243891
Authors: Haoming Tan, Junyun Wu, Hongjie Guan, Zhijun Zhang, Lin Tao, Qingmin Zhao, Chunquan Li
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,955
OpenAlex ID: W4405235652