基于改进灰色模型和RBF优化模型的导弹贮存寿命预测

针对导弹在贮存期间存在的故障数据量少、预测难度大的问题，在给出灰色模型和RBF神经网络优化模型的基础上，构建基于灰色模型和RBF神经网络优化模型的组合预测模型，并利用该模型对某型导弹贮存寿命进行预测。结果表明，组合模型对小样本数据具有较高的预测精度，克服了单一预测模型的不足，具有很强的实用性。 

Storage Life Forecasting for Missiles Based on Improved Gray Model and RBF Optimization Model

The combined forcasting model based on the gray model and RBF neural network optimization model was proposed, for solving the problem of little failure data in storage period and difficult to forecast, and the combined model was established to forecast the storage life of missiles. The result shows that the combined model is better than the single forecasting model, and is regarded of high practice value. 