Effectiveness evaluation for UAV air-to-ground attack based on PSO-BP neural network

In order to adapt to the increasingly rapid air combat rhythm and provide key basis for optimal operational decision, how to evaluate the operational effectiveness of unmanned aerial vehicle (UAV) quickly and accurately is a basic and important research topic for UAV combat system. Considering the problem of effectiveness evaluation, this paper presents an effectiveness evaluation model for UAV air-to-ground attack which is based on PSO-BP neural network. Firstly, the main influencing factors of effectiveness evaluation of air-to-ground attack are analyzed and summarized. Secondly, the basic principle of PSO algorithm and BP neural network are introduced respectively and then PSO-BP neural network is constructed. Finally, simulation experiments are performed using historical statistics and the simulation results show that the proposed method can reduce the training error and improve the evaluation accuracy, which implies that the proposed algorithm is a better method to solve the problem of effectiveness evaluation of UAV air-to-ground attack.