scholarly journals Accelerating noise-tolerant zeroing neural network with fixed-time convergence to solve the time-varying Sylvester equation

Automatica ◽  
2022 ◽  
Vol 135 ◽  
pp. 109998
Author(s):  
Miaomiao Zhang ◽  
Bing Zheng
Keyword(s):  
Neural Network ◽  
Fixed Time ◽  
Sylvester Equation ◽  
Time Varying ◽  
Noise Tolerant

scholarly journals A varying-gain ZNN model with fixed-time convergence and noise-tolerant performance for time-varying linear equation and inequality systems

2021 ◽  
Author(s):  
Miaomiao Zhang
Keyword(s):  
Neural Network ◽  
Linear Equation ◽  
Design Parameter ◽  
Nonlinear Function ◽  
Fixed Time ◽  
Time Varying ◽  
Bounded Noise ◽  
Inequality Systems ◽  
Zhang Neural Network ◽  
Noise Tolerant

<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>

scholarly journals A varying-gain ZNN model with fixed-time convergence and noise-tolerant performance for time-varying linear equation and inequality systems

2021 ◽  
Author(s):  
Miaomiao Zhang
Keyword(s):  
Neural Network ◽  
Linear Equation ◽  
Design Parameter ◽  
Nonlinear Function ◽  
Fixed Time ◽  
Time Varying ◽  
Bounded Noise ◽  
Inequality Systems ◽  
Zhang Neural Network ◽  
Noise Tolerant

<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>

A New Varying-Parameter Recurrent Neural-Network for Online Solution of Time-Varying Sylvester Equation

2018 ◽  
Vol 48 (11) ◽  
pp. 3135-3148 ◽  
Author(s):  
Zhijun Zhang ◽  
Lunan Zheng ◽  
Jian Weng ◽  
Yijun Mao ◽  
Wei Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Sylvester Equation ◽  
Time Varying

scholarly journals An Improved Complex-Valued Recurrent Neural Network Model for Time-Varying Complex-Valued Sylvester Equation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 19291-19302 ◽  
Author(s):  
Lei Ding ◽  
Lin Xiao ◽  
Kaiqing Zhou ◽  
Yonghong Lan ◽  
Yongsheng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Sylvester Equation ◽  
Time Varying ◽  
Complex Valued
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