检索结果-内蒙古大学图书馆

Centralized and Collective Neurodynamic Optimization Approaches for Sparse Signal Reconstruction via l1Y-minimization

引用

IEEE TRANSACTIONS ON NEURAl NETWORKS AND lEARNING SYSTEMS 2022年第12期33卷 7488-7501页

作者： Zhao, You liao, Xiaofeng He, Xing Tang, Rongqiang 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

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.

关键词： Centralized and collective neurodynamic approaches global convergence l-1-minimization problem sparse signal reconstruction

来源：评论

学校读者我要写书评

暂无评论

A Two-layer Distributed Algorithm Using Neurodynamic System for Solving l1-minimization

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第8期69卷 3490-3494页

作者： Xu, Junpeng He, Xing Han, Xin Wen, Hongsong Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

This brief considers a distributed algorithm for solving l-1-minimization problem based on nonlinear neurodynamic system. Compared with centralized algorithms, distributed algorithms have great potential in data privacy protection, distributed storage and processing of data. In this brief, l-1-minimization problem is transformed into a distributed problem by using multiagent consensus theory. For the distributed optimization problem, a two-layer distributed algorithm is designed by utilizing neurodynamic system, projection matrix and derivative feedback technique. Compared with the existing distributed neurodynamic algorithm, the proposed algorithm has a simpler structure and has fewer neurons on the premise that the calculation error does not increase. Besides, the proposed algorithm converges to a minimal point of l-1-minimization problem and is lyapunov stable. Finally, the comparative examples of sparse signal reconstruction show that the proposed distributed algorithm is effective and superior.

关键词： l-1-minimization problem sparse signal reconstruction distributed algorithm neurodynamic system

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：