Abstarct: Abstract Two neural network models are constructed to solve convex multiobjective programming problem. The convex multiobjective programming problem is first converted into an equivalent single-objective convex programming problem by the mean of the weighted sum method, where the Pareto optimal solutions are given by diversifying values of weights. Then, for given various values weights, neural networks are employed to search for Pareto optimal solutions. baxsed on employing Lyapunov theory, the proposed neural network approach is established to be stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the single-objective problem. The simulation results also show that the presented model is feasible and efficient. Furthermore, we use two recurrent neural networks solving convex optimization problems with fuzzy parameters. Since there is a few research on resolving fuzzy optimization problems by recurrent neural networks, we establish a new scheme to solve the problem. By reducing the original program to an interval problem and then a weighting problem, the Karush-Kuhn-Tucker conditions are presented. Moreover, we apply the Karush-Kuhn-Tucker conditions to recurrent neural networks as an efficient tool to solve the problem. Besides, the convergence properties and stability analysis of the system models are provided. In the final step, several simulation examples are verified to support the obtained results. Reported results are compared with some other previous neural networks.