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

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posted on 16.11.2021, 22:33 by Miaomiao ZhangMiaomiao Zhang
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
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.

Funding

National Natural Science Foundation of China (Grant No.12071196)

History

Email Address of Submitting Author

mzhang19@lzu.edu.cn

Submitting Author's Institution

the School of Mathemat- ics and Statistics, Lanzhou University

Submitting Author's Country

China