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A varying-parameter fixed-time gradient-based dynamic network for convex optimization
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DOI：10.1016/j.neunet.2023.08.047.png)
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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.(c) 2023 Elsevier Ltd. All rights reserved.
Keyword:
Gradient-based dynamic network
Activation function
Fixed-time convergence
Time-varying scaling parameter
Unbounded noise
期刊
IF:
6.3
论文数:
8.2K
被引数:
3.0W
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A gradient-type noise-tolerant finite-time neural network for convex optimization.png)
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