Effectiveness Evaluation of UUV Cooperative Combat Based on GAPSO-BP Neural Network
Yuanming Ding, Chengyang LIU, Qian Lu, Zhu Min · 2019
Underwater unmanned vehicle (UUV) cooperative combat is an important pattern of underwater warfare.. Aiming at the problem of effectiveness evaluation, this paper presents an effectiveness evaluation model based on GAPSO-BP neural network, which can acquire the information of the expert by training, and calculate the effectiveness value fast and continuously. Firstly, according to the indicator system of UUV Cooperative Combat Effectiveness Evaluation, the evaluation system combined with AHP method improved by intuitionistic fuzzy and BP neural network is given. Secondly, by analyzing GA and PSO method, and combing their advantages, the GAPSO-BP neural network is constructed. Finally, simulation experiments are performed using simulation examples and the simulation results show that the model proposed can evaluate the UUV cooperative combat effectiveness accurately and effectively, and the iterative convergence of GAPSO optimization algorithm is superior to GA, PSO algorithm, and its Evaluation accuracy is the best.