Using Laplacian Eigenmap as Heuristic Information to Solve Nonlinear Constraints Defined on a Graph and Its Application in Distributed RangeFree Localization of Wireless Sensor Networks
 Shuai Li,
 Zheng Wang,
 Yangming Li
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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 continuoustime projected neural network, and the corresponding discretetime 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 rangefree localization of wireless sensor networks. Simulations demonstrate the effectiveness of the proposed methods.
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 Title
 Using Laplacian Eigenmap as Heuristic Information to Solve Nonlinear Constraints Defined on a Graph and Its Application in Distributed RangeFree Localization of Wireless Sensor Networks
 Journal

Neural Processing Letters
Volume 37, Issue 3 , pp 411424
 Cover Date
 20130601
 DOI
 10.1007/s1106301292558
 Print ISSN
 13704621
 Online ISSN
 1573773X
 Publisher
 Springer US
 Additional Links
 Topics
 Keywords

 Projected dynamic neural network
 Constrained optimization
 Laplacian eigenmap
 Wireless sensor networks
 Industry Sectors
 Authors

 Shuai Li ^{(1)}
 Zheng Wang ^{(2)}
 Yangming Li ^{(3)}
 Author Affiliations

 1. Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA
 2. Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, M5S3G4, Canada
 3. Robot Sensor and HumanMachine Interaction Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, 230031, Anhui, China