Byp-power (or partial p-power) transformation, the Lagrangian function in nonconvex optimization problem becomes locally convex. In this paper, we present a neural network based on an NCP function for solving nonconvex optimization problem. One of the important features of this neural network is the one-to-one correspondence between its equilibria and KKT points of the nonconvex optimization problem; in the other words, the neural network is proved to be stable and convergent to an optimal solution of the original problem. Finally, examples are provided to show the applicability of the proposed neural network.

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