Using laplacian eigenmap as heuristic information to solve nonlinear constraints defined on a graph and its application in distributed range-free localization of wireless sensor networks

Shuai Li, Zheng Wang, Yangming Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

34 Citations (Scopus)

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 languageEnglish
Pages (from-to)411-424
Number of pages14
JournalNeural Processing Letters
Volume37
Issue number3
DOIs
Publication statusPublished - 1 Jun 2013
Externally publishedYes

Keywords

  • Constrained optimization
  • Laplacian eigenmap
  • Projected dynamic neural network
  • Wireless sensor networks

ASJC Scopus subject areas

  • Software
  • Neuroscience(all)
  • Computer Networks and Communications
  • Artificial Intelligence

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