In the present research, we obtain the solution of the fuzzy nonlinear programming problems -FNLPs- using recurrent neural network models. Since there is a few study and research for resolving of FNLP by recurrent neural network models, we obtain a new strategy to solve the problem. Reformulating the original problem to an interval program and then weighting problem, the Karush–Kuhn–Tucker -KKT- optimality conditions are presented. Moreover, we apply the KKT conditions into a recurrent neural network model as a high-performance tool to solve the problem. Besides, the global convergence and the Lyapunov stability of the system are proved in this research. In the final step, several illustrative examples are given to substantiate the obtained results. Reported results are compared with other published papers.

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