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• W4386475655 abstract "We focus on the fixed-time convergence and robustness of gradient-based dynamic networks for solving convex optimization. Most of the existing gradient-based dynamic networks with fixed-time convergence have limited ability to resist interferences of noises. To improve the convergence of the gradient-based dynamic networks, we design a new activation function and propose a gradient-based dynamic network with fixed-time convergence. The proposed dynamic network has a smaller upper bound of the convergence time than the existing dynamic networks with fixed-time convergence. A time-varying scaling parameter is employed to speed up the convergence. Our gradient-based dynamic network is proved to be robust against bounded noises and is able to resist the interference of unbounded noises. The numerical tests illustrate the effectiveness and superiority of the proposed network." @default.
• W4386475655 created "2023-09-07" @default.
• W4386475655 creator A5022573770 @default.
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• W4386475655 date "2023-10-01" @default.
• W4386475655 modified "2023-10-18" @default.
• W4386475655 title "A varying-parameter fixed-time gradient-based dynamic network for convex optimization" @default.
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• W4386475655 doi "https://doi.org/10.1016/j.neunet.2023.08.047" @default.
• W4386475655 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37738715" @default.
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