Hopfield introduced the neural network for linear programming with linear constraints. In this paper, Hopfield neural network has been generalized to solve the optimization problems including nonlinear constraints. The proposed neural network can solve a nonlinear cost function with nonlinear constraints. Also, methods have been discussed to reconcile optimization problems with neural networks and implementation of the circuits. Simulation results show that the computational energy function converges to stable point by decreasing the cost function as the time passes.