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Solving Fuzzy Convex Programming Problems via a Projection Neural Network Framework

Author

Listed:
  • Mohammadreza Jahangiri

    (Faculty of Mathematical Sciences, Shahrood University of Technology, P. O. Box 3619995161-316, Shahrood, Iran)

  • Alireza Nazemi

    (Faculty of Mathematical Sciences, Shahrood University of Technology, P. O. Box 3619995161-316, Shahrood, Iran)

Abstract

In this paper, the solution of the fuzzy nonlinear optimization problems (FNLOPs) is given using a projection recurrent neural network (RNN) scheme. Since there is a few research for resolving of FNLOP by RNNs, we describe a new framework to solve the problem. By reformulating the original program to an interval problem and then weighting problem, the Karush–Kuhn–Tucker (KKT) conditions are obtained. Moreover, we utilize the KKT conditions into a RNN as a capable tool to solve the problem. Besides, the global convergence and the Lyapunov stability of the neuro-dynamic model are established. In the final step, some simulation examples are stated to validate the obtained results. Reported results are compared with some other previous neural networks.

Suggested Citation

  • Mohammadreza Jahangiri & Alireza Nazemi, 2025. "Solving Fuzzy Convex Programming Problems via a Projection Neural Network Framework," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 21(01), pages 159-193, March.
    • Handle: RePEc:wsi:nmncxx:v:21:y:2025:i:01:n:s1793005725500103
    DOI: 10.1142/S1793005725500103
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