摘要
针对指挥决策者难以从复杂多变的战场态势要素中获取关联信息从而预判战场态势发展趋势的问题,将战场态势视为灰色系统,利用灰色系统理论对战场态势要素进行分类,分析要素之间的关联程度,从而为指挥决策者提供决心依据。结合神经网络建立GM(1,1)-RBF,DGM(2,1)-RBF组合模型对战场态势要素进行预测。同时,为提高模型预测性能,使用改进的算法对组合模型中的单个模型进行了优化。仿真实验结果表明,优化后的组合模型在预测精度及算法效率上均有明显提升。
It is difficult for the commander and decision-maker to obtain the related information from complicated battlefield situational elements for judging the trend of battlefield. Aiming at this problem, we regarded the battlefield situation as a grey system, used the grey system theory to classify the acquisition of situational elements, and analyzed the degree of correlation between the elements to provide the basis for the commander to make decision. The data of battlefield situational elements was predicted by establishing a combined model of GM（ 1. 1）-RBF model and DGM（ 2, 1）-RBF models. To increase the prediction performance,the improved algorithm was used to optimize the single model of the combined models. Simulation result shows that the optimized combined models has much higher prediction accuracy and efficiency.
出处
《电光与控制》
北大核心
2015年第12期15-19,23,共6页
Electronics Optics & Control
基金
全军军事类研究生资助课题(13QJ003-22)
关键词
战场态势
分析及预测
灰色神经网络
battlefield situation
analysis and prediction
grey neural network
