Centralized and Collective Neurodynamic Optimization Approaches for Sparse Signal Reconstruction via L1Y-Minimization

作     者：Zhao, You Liao, Xiaofeng He, Xing Tang, Rongqiang 

作者机构：Chongqing Univ Coll Comp Sci Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Sch Elect & Informat Engn Chongqing 400715 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS》 (IEEE Trans. Neural Networks Learn. Sys.)

年 卷 期：2022年第33卷第12期

页      面：7488-7501页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Key Research and Development Program of China [2018AAA0100101] National Natural Science Foundation of China [61932006, 61772434] Chongqing Technology Innovation and Application Development Project [cstc2020jscx-msxmX0156] Fundamental Research Funds for the Central Universities [XDJK2020TY003] 

主　　题：Centralized and collective neurodynamic approaches global convergence L-1-minimization problem sparse signal reconstruction 

摘      要：This article develops several centralized and collective neurodynamic approaches for sparse signal reconstruction by solving the L-1-minimization problem. First, two centralized neurodynamic approaches are designed based on the augmented Lagrange method and the Lagrange method with derivative feedback and projection operator. Then, the optimality and global convergence of them are derived. In addition, considering that the collective neurodynamic approaches have the function of information protection and distributed information processing, first, under mild conditions, we transform the L-1-minimization problem into two network optimization problems. Later, two collective neurodynamic approaches based on the above centralized neurodynamic approaches and multiagent consensus theory are proposed to address the obtained network optimization problems. As far as we know, this is the first attempt to use the collective neurodynamic approaches to deal with the L-1-minimization problem in a distributed manner. Finally, several comparative experiments on sparse signal and image reconstruction demonstrate that our proposed centralized and collective neurodynamic approaches are efficient and effective.