Abstract
In this paper, we are concerned with the problem of nonlinear inequalities defined on a graph. The feasible solution set to this problem is often infinity and Laplacian eigenmap is used as heuristic information to gain better performance in the solution. A continuous-time projected neural network, and the corresponding discrete-time projected neural network are both given to tackle this problem iteratively. The convergence of the neural networks are proven in theory. The effectiveness of the proposed neural networks are tested and compared with others via its applications in the range-free localization of wireless sensor networks. Simulations demonstrate the effectiveness of the proposed methods.
Original language | English |
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Pages (from-to) | 411-424 |
Number of pages | 14 |
Journal | Neural Processing Letters |
Volume | 37 |
Issue number | 3 |
DOIs | |
Publication status | Published - 1 Jun 2013 |
Externally published | Yes |
Keywords
- Constrained optimization
- Laplacian eigenmap
- Projected dynamic neural network
- Wireless sensor networks
ASJC Scopus subject areas
- Software
- General Neuroscience
- Computer Networks and Communications
- Artificial Intelligence