基于灰色BP神经网络的装备保障费用预测模型

装备的保障费用是装备全寿命周期费用的重要组成部分,为了科学合理地预测武器装备的保障费用,通过分析装备保障费用的构成及其影响因素,考虑到装备保障费用数据量有限、复杂多变、非线性,用单一预测模型预测精度不高,因此建立了基于灰色系统理论和BP神经网络的组合预测模型,将灰色系统模型善于处理小样本数据和BP神经网络优于解决复杂非线性问题的优点有效地结合起来,对基于非线性时间序列的保障费用进行预测。仿真实例表明该组合模型的预测结果比传统单一模型所得到的预测结果总体误差要小,可以有效提高装备保障费用的预测精度。

Equipment Support Cost Forecasting Model Based on Grey System Theory and BP Neural Network

The support cost is the main part of LCC(life cycle cost) of weapon equipment, For the purpose of reasonably forecasting the support cost of weapon equipment, the paper analyses the constitution of equipment support cost and its affecting factors. Given the limitation, complexity and nonlinearity of date quantity of equipment support cost, the forecasting precision of single model is low. So the paper puts forward a combined forecasting model based on grey system theory and BP neural network model to forecast the support cost which are based on nonlinear time series, which put the advantage that grey system theory can easily do the small sample data together with the advantage that BP neural network model can solve the complex and nonlinear problem. A simulation example proves that the total error of the combined model''s forecasting result is smaller than that of single model''s forecasting result. This can largely improve the forecasting precision of equipment support cost.