ICANN 98 pp 547-553 | Cite as

A Linear Programming Neural Circuit Model

  • József Bíró
  • Miklós Boda
Conference paper
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)


In this paper we present a neural circuit model which can solve linear programming problems. The main feature of the model is that it takes into account saturating behaviour of circuit elements in a possible realizations. The neural model can be viewed as a gradient system and its operation is based on the penalty function approach of solving linear programming tasks. In the paper the properties of the model are discussed including stability and that how to utilize the saturation in the neurons for obtaining better performance.


Neural Network Penalty Function Linear Programming Problem Recurrent Neural Network Saturation Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    J. Bíró, E. Halasz, T. Trón, M. Boda, and G. Privitzky. Neural networks for exact constrained optimization. In Lecture Notes in Computer Science (Proceedings of the International Conference on Artificial Neural Networks-ICANN’96), Bochum (Germany), June 1996.Google Scholar
  2. [2]
    J. Bíró, Z. Koronkai, L. Ast, T. Trón, and M. Boda. Analyses of extended and generalized optimization neural networks. Journal of Artificial Neural Networks, 2(4):401–409, August 1995.Google Scholar
  3. [3]
    L.O. Chua and G. Lin. Nonlinear programming without computation. IEEE Trans, on Circuits and Systems, 52(2), February 1984.Google Scholar
  4. [4]
    A. Cichocki and R. Unbehauen. Neural networks for solving systems of linear equations and related problems. IEEE Trans, on Circuits and Systems, 39(2): 124–138, February 1992.MATHCrossRefGoogle Scholar
  5. [5]
    M.P. Kennedy and L.O. Chua. Neural networks for nonlinear programming. IEEE Trans, on Circuits and Systems, 35(5):554–562, May 1988.MathSciNetCrossRefGoogle Scholar
  6. [6]
    S. Hui S. H. Zak, V. Upatising. Solving linear programming problems with neural networks: A comparative study. IEEE Trans, on Neural Networks, 6(1):94–104, 1995.CrossRefGoogle Scholar
  7. [7]
    K. Zikan T.P. Caudell. A neural network architecture for linear programming. In Proc. of IEEE ICNN’92, pages 91–96, October 1992.Google Scholar
  8. [8]
    J. Wang. Analysis and design of a recurrent neural network for linear programming. IEEE Trans, on Circuits and Systems, 40(9):613–618, September 1993.MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 1998

Authors and Affiliations

  • József Bíró
    • 1
  • Miklós Boda
    • 2
  1. 1.High Speed Networks Laboratory Dept. of Telecommunications and TelematicsTechnical University of BudapestBudapestHungary
  2. 2.Traffic Analysis and Network Performance LaboratoryEricssonBudapestHungary

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