关键词: 威胁估计, BP神经网络, 全局优化

Abstract:

On the basis of the characteristics of aerial targets threat assessment, the weaknesses of BP neural network for aerial targets threat assessment are analyzed. By using the ant colony optimization (ACO) algorithm seeking global excellent result to optimize the random of BP algorithm, a new aerial targets threat assessment method is established and the ACOBP algorithm is achieved by the method, which overcomes the randomness of BP network initial weights, solves the problem lost in local minimum, and improves convergence speed of the network. Finally, the performance of the algorithm is analyzed. Simulation results show the ACOBP algorithm can estimate threat degree accurately and appropriately with faster convergence and better performance than the BP algorithm, proving that the ACOBP algorithm is an effective approach to threat assessment.

Key words: threat assessment, BP neural network, global optimization