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

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.

Automated summary

This paper proposes a method for sparse signal and image reconstruction using projection neural networks. By designing the finite-time converging FtPNN and fixed-time converging FxtPNN, the stability and convergence properties are proven under specific conditions. Simulation examples are carried out to demonstrate the effectiveness of these two neural networks.