一种改进的装备保障能力并联预测模型

An Improved Parallel Prediction Model of Equipment Support Capability

为了提高装备保障能力的预测精度,针对当前预测算法及其组合模型存在的问题,提出了一种改进的并联预测模型。利用文本挖掘选择预测指标及权重,改进了区间标度算法并构造了不等距、多尺度区间的模糊时间序列模型。改进了粒子群优化方法中微粒速度和位置及惯性权重值的算法,使用该方法优化了支持向量机参数并建立预测模型。依据改进的模糊时间序列和支持向量机预测模型建立了改进的并联预测模型,通过计算预测权重值并将预测值与预测权重值组合形成并联模型的预测值。通过案例证明了该预测方法具有更高的精度。

Equipment support capability is an important constituent part of army combat power. For the sake of improving the accuracy of predicting the equipment support capability,an improved parallel prediction model is proposed for the problems existing in current prediction algorithms and their combined models. The indexes and weights of equipment support capability are confirmed by text mining. A non-isometric multi-scale interval fuzzy time series model is established by modifying the interval scale algorithm. At the same time,the particle swarm optimization algorithms about particle speed,location and inertia weight value are improved to optimize the parameters of support vector machine. An improved parallel prediction model is constructed based on the above two models,by which the weight values are calculated and the predicted weight values and the predicted values are combined to obtain the final predictive value. The given example shows that the improved prediction model is accurate.