To address the challenge of the real-time weapon target intelligent assignment in modern high dynamic air defense operation, a neural network based air defense weapon target intelligent assignment method is proposed in this paper. An optimization model for weapon target assignment is first established considering the number of weapons and interception capability in order to optimize the damage efficiency. A neural network based weapon target intelligent assignment framework is then developed, where a neural network is constructed based on the existed training sample set and employed to assign the weapons to the targets effectively and efficiently. The comparison results illustrate that the neural network based air defense weapon target assignment method can obtain better weapon target assignment solution compared with the conventional discrete differential evolution based assignment method. Moreover, the computational cost of the proposed method can be reduced by 99. 9%, which demonstrates the effectiveness and practicability of the research work.
