This paper introduces a new Hopfiled neural network for nonlinear constrained optimization problem based on penalty function with objective parameters. The energy function for the neural network with its neural dynamics is defined, which differs from some known Hopfiled neural networks. The system of the neural networks is stable, and its equilibrium point of the neural dynamics corresponds to a solution for the nonlinear constrained optimization problem under some condition. Based on the relationship between the equilibrium points and the energy function, an algorithm is developed for computing an equilibrium point of the system or an optimal solution to its optimization problem. One example is given to show the efficiency of the algorithm.