Discrete neural network for solving general quadratic programming problems

  • Authors:

  • Valeri Mladenov

  • Affiliations:

  • Department of Theoretical Electrical Engineering, Faculty of Automatics, Technical University of Sofia, Sofia, Bulgaria

  • Venue:
  • ICS'05 Proceedings of the 9th WSEAS International Conference on Systems
  • Year:
  • 2005

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Abstract

Quadratic programming problems are widespread class of nonlinear programming problems with many practical applications. The case of inequality constraints have been considered in a previous author's paper. Later on an extension of these results for the case of inequality and equality constraints has been proposed. Based on equivalent formulation of Kuhn-Tucker conditions, a new neural network for solving the general quadratic programming problems, for the case of both inequality and equality constraints has been presented. In this contribution a discrete version of this network is proposed. Two theorems for global stability and convergence of this network are given as well. The presented network has lower complexity and it is suitable for FPGA implementations. Simulation results based on SIMULINK® models are given and compared.