基于AGABP的武器装备效能组合预测

Combinatorial Forecasting of Weapon Equipment Operational Effectiveness Based on AGABP

针对海洋环境影响下武器装备作战效能的评估问题,建立了基于BP神经网络的组合预测评估模型。利用神经网络,将多个方案的预测结果作为网络输入,其真实值作为输出,以组合预测模型均方误差最小为目标,属于一种非线性组合预测模型。同时针对BP网络收敛速度缓慢,容易形成局部极小解的缺点,提出了AGABP(自适应遗传反向传播)算法对其进行改进。最后针对某作战平台作战效能的评估实例,在多种单项预测模型的基础上利用AGABP算法进行组合预测,得到了较好的实验结果。

The combinational forecasting model based on BP neural network is established to evaluate the operational effectiveness of weapon equipment under the influence of marine environment. The model takes advantages of neural network, and takes the prediction results of multiple solutions as the inputs and the true values as output of neural network aiming at getting the smallest mean squared error. It is a kind of the nonlinear combination forecasting model. To solve the problem of BP network slowly converging and easily falling into the local minimum points, an algorithm called AGABP （Adaptive Genetic Algorithm based Back Propagation） is proposed to improve it. Finally, aiming at the operational effectiveness evaluation instance of an operation platform and based on several single-forecasting-models,the AGABP model is used for combinational forecasting and gets good experimental results.