Junpeng Xu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Junpeng Xu has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Xing He
Southwest University
2 shared publications - Hongsong Wen
Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, China
1 shared publication Xin Han
Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, China
1 shared publication
Latest Publications
- 10 January 2024
- Published by Elsevier in Neural Networks
- Vol. 172, 106123
Distributed continuous-time accelerated neurodynamic approaches for sparse recovery via smooth approximation to -minimization- 10 January 2024
- Published by Elsevier in Neural Networks
- Vol. 172, 106123
Distributed continuous-time accelerated neurodynamic approaches for sparse recovery via smooth approximation to -minimization- 16 March 2022
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Circuits and Systems II: Express Briefs
- Vol. 69 (8) , 3490-3494
A Two-Layer Distributed Algorithm Using Neurodynamic System for Solving L 1-Minimization
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.
- 16 March 2022
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Circuits and Systems II: Express Briefs
- Vol. 69 (8) , 3490-3494
A Two-Layer Distributed Algorithm Using Neurodynamic System for Solving L 1-Minimization
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.