An efficient modified neural network for solving nonlinear programming problems with hybrid constraints

This paper presents ‎‎the optimization techniques for solving‎‎ convex programming problems with hybrid constraints‎.‎ According to the saddle point theorem‎, ‎optimization theory‎, ‎convex analysis theory‎, ‎Lyapunov stability theory and LaSalle‎‎invariance principle‎,‎ a neural network model is constructed‎.‎ The equilibrium point of the proposed model is proved to be equivalent to the optimal ‎‎solution of the original problem‎. ‎It is also shown that the proposed network model is stable in the Lyapunov sense and it is globally convergent to an exact optimal solution of the original problem‎. ‎Several practical examples are provided to show the feasibility and the efficiency of the‎method.