Linear Programming Problem for Nonlinear Convex Set Constraints Based on Nonlinear Neural Network

Linear programming problem is widely applied in engineering group. And artificial neural network is an effective and practical method and approach for solving linear programming problem of nonlinear convex set constraints in engineering field. Most models of artificial neural network are nonlinear dynamic system. If the objective function of optimization calculation problem is corresponding to some energy function of network, steady state point is the local or global optimal dynamic process solution for optimizing the problem. This paper studied the optimization problem with nonlinear constrained convex set by the application of nonlinear neural network. First, the inverse optimization problem with nonlinear convex set constraints into nonlinear bilevel programming problem. Then the optimization solution was searched by establishing neural network model. In addition, the stability of model was analyzed and sufficient conditions for global asymptotic stability of the balance point were given. Finally, the validity and effectiveness of the conclusion was verified by example.