College of Electronic and Information Engineering and Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, Chongqing 400715, China.
Department of Computer Science and School of Data Science, City University of Hong Kong, Kowloon, Hong Kong.
Neural Netw. 2022 Oct;154:255-269. doi: 10.1016/j.neunet.2022.07.018. Epub 2022 Jul 19.
In this paper, we formulate a mixed-integer problem for sparse signal reconstruction and reformulate it as a global optimization problem with a surrogate objective function subject to underdetermined linear equations. We propose a sparse signal reconstruction method based on collaborative neurodynamic optimization with multiple recurrent neural networks for scattered searches and a particle swarm optimization rule for repeated repositioning. We elaborate on experimental results to demonstrate the outperformance of the proposed approach against ten state-of-the-art algorithms for sparse signal reconstruction.
在本文中，我们针对稀疏信号重建问题建立了一个混合整数问题，并将其重新表述为一个带有替代目标函数的全局优化问题，同时满足欠定线性方程组的约束条件。我们提出了一种基于多递归神经网络的协同神经动力学优化稀疏信号重建方法，用于分散搜索，以及基于粒子群优化规则的重复定位。我们详细阐述了实验结果，以证明与稀疏信号重建的十种最先进算法相比，所提出的方法具有优越性。
