基于BP 神经网络的舰载机对陆打击作战效能评估

为解决复杂战场态势下影响舰载机对陆打击作战效能因素多、情况复杂的问题，提出一种基于BP 神经 网络学习算法的舰载机对陆打击作战效能评估模型。结合舰载机性能及战场环境，运用层次分析法构建舰载机对陆 打击作战效能评估指标体系；通过Matlab 工具进行动态评估仿真。仿真结果表明：该模型准确率能达到98.5%，验 证了模型的有效性和可行性，可为舰载机在战术应用方面提供一定的决策信息。

Operational Effectiveness Evaluation of Carrier-based Aircraft Attacking LandBased on BP Neural Network

In order to solve the problem that there are many factors affecting the operational effectiveness of carrier-based aircraft attacking land in complex battlefield situation, a operational effectiveness evaluation model of carrier-based aircraft attacking land based on BP neural network learning algorithm is proposed. Combined with the performance of carrier-based aircraft and battlefield environment, the evaluation index system of operational effectiveness of carrier-based aircraft attacking land was established by using analytic hierarchy process (AHP). The dynamic evaluation simulation is carried out by Matlab tool. The simulation results show that the model accuracy rate reaches 98.5%, and results also verify the validity and feasibility of the model, and can provide some decision-making information for the tactical application of carrier-based aircraft.