A Two-Layer Distributed Algorithm Using Neurodynamic System for Solving L 1 -Minimization
This brief considers a distributed algorithm for solving [Formula Omitted]-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 br...
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| Vydáno v: | IEEE transactions on circuits and systems. II, Express briefs Ročník 69; číslo 8; s. 3490 - 3494 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
01.08.2022
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| Témata: | |
| ISSN: | 1549-7747, 1558-3791 |
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| Abstract | This brief considers a distributed algorithm for solving [Formula Omitted]-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, [Formula Omitted]-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 [Formula Omitted]-minimization problem and is Lyapunov stable. Finally, the comparative examples of sparse signal reconstruction show that the proposed distributed algorithm is effective and superior. |
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| AbstractList | This brief considers a distributed algorithm for solving [Formula Omitted]-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, [Formula Omitted]-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 [Formula Omitted]-minimization problem and is Lyapunov stable. Finally, the comparative examples of sparse signal reconstruction show that the proposed distributed algorithm is effective and superior. |
| Author | He, Xing Xu, Junpeng Han, Xin Wen, Hongsong |
| Author_xml | – sequence: 1 givenname: Junpeng surname: Xu fullname: Xu, Junpeng organization: Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, China – sequence: 2 givenname: Xing orcidid: 0000-0001-8652-8479 surname: He fullname: He, Xing organization: Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, China – sequence: 3 givenname: Xin surname: Han fullname: Han, Xin organization: Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, China – sequence: 4 givenname: Hongsong surname: Wen fullname: Wen, Hongsong organization: Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, China |
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| Title | A Two-Layer Distributed Algorithm Using Neurodynamic System for Solving L 1 -Minimization |
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