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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 1/2024

29.10.2023 | Original Article

Projection neural networks with finite-time and fixed-time convergence for sparse signal reconstruction

verfasst von: Jing Xu, Chuandong Li, Xing He, Xiaoyu Zhang

Erschienen in: Neural Computing and Applications | Ausgabe 1/2024

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Abstract

This paper considers the \(L_1\)-minimization problem for sparse signal and image reconstruction by using projection neural networks (PNNs). Firstly, a new finite-time converging projection neural network (FtPNN) is presented. Building upon FtPNN, a new fixed-time converging PNN (FxtPNN) is designed. Under the condition that the projection matrix satisfies the Restricted Isometry Property (RIP), the stability in the sense of Lyapunov and the finite-time convergence property of the proposed FtPNN are proved; then, it is proven that the proposed FxtPNN is stable and converges to the optimum solution regardless of the initial values in fixed time. Finally, simulation examples with signal and image reconstruction are carried out to show the effectiveness of our proposed two neural networks, namely FtPNN and FxtPNN.
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Metadaten
Titel
Projection neural networks with finite-time and fixed-time convergence for sparse signal reconstruction
verfasst von
Jing Xu
Chuandong Li
Xing He
Xiaoyu Zhang
Publikationsdatum
29.10.2023
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 1/2024
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-023-09015-9

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