A Gain-Adjustment Neural Network Based Time-Varying Underdetermined Linear Equation Solving Method

2021 ◽  
Author(s):  
Zhijun Zhang ◽  
Lunan Zheng ◽  
Tairu Qiu
2021 ◽  
Author(s):  
Miaomiao Zhang

<div>In this paper, a varying-gain zeroing (or Zhang) neural network (VG-ZNN) is proposed to obtain the online solution of the time-varying linear equation and inequality system. Distinguished from the fixed-value design parameter in</div><div>the original zeroing (or Zhang) neural network (ZNN) models, the design parameter of the VG-ZNN model is a nonlinear function that changes with time. The VG-ZNN model composed of the new time-varying design parameter we proposed can achieve fixed-time convergence and tolerate time-varying bounded noise and time-varying derivable noise. The theoretical detailed analysis of the convergence and robustness of the VG-ZNN model are given.</div>


2019 ◽  
Vol 30 (8) ◽  
pp. 2346-2357 ◽  
Author(s):  
Feng Xu ◽  
Zexin Li ◽  
Zhuoyun Nie ◽  
Hui Shao ◽  
Dongsheng Guo

2021 ◽  
Author(s):  
Miaomiao Zhang

<div>In this paper, a varying-gain zeroing (or Zhang) neural network (VG-ZNN) is proposed to obtain the online solution of the time-varying linear equation and inequality system. Distinguished from the fixed-value design parameter in</div><div>the original zeroing (or Zhang) neural network (ZNN) models, the design parameter of the VG-ZNN model is a nonlinear function that changes with time. The VG-ZNN model composed of the new time-varying design parameter we proposed can achieve fixed-time convergence and tolerate time-varying bounded noise and time-varying derivable noise. The theoretical detailed analysis of the convergence and robustness of the VG-ZNN model are given.</div>


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