An improved dual neural network for solving a class of quadratic programming problems and its k-winners-take-all application

Xiaolin Hu, Jun Wang

Research output: Contribution to journalArticlepeer-review

151 Citations (Scopus)

Abstract

This paper presents a novel recurrent neural network for solving a class of convex quadratic programming (QP) problems, in which the quadratic term in the objective function is the square of the Euclidean norm of the variable. This special structure leads to a set of simple optimality conditions for the problem, based on which the neural network model is formulated. Compared with existing neural networks for general convex QP, the new model is simpler in structure and easier to implement. The new model can be regarded as an improved version of the dual neural network in the literature. Based on the new model, a simple neural network capable of solving the k-winners-take-all (k-WTA) problem is formulated. The stability and global convergence of the proposed neural network is proved rigorously and substantiated by simulation results.

Original languageEnglish
Pages (from-to)2022-2031
Number of pages10
JournalIEEE Transactions on Neural Networks
Volume19
Issue number12
DOIs
Publication statusPublished - 2008
Externally publishedYes

Keywords

  • Global asymptotic stability
  • k-winners-take-all (k-WTA)
  • Optimization
  • Quadratic programming (QP)
  • Recurrent neural network

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