基于改进最小二乘支持向量机的导弹备件消耗预测

在系统分析武器装备备件预测方法研究现状和导弹备件消耗特点的基础上,提出把粗糙集、熵权法、自适应粒子群优化算法与加权最小二乘支持向量机的组合预测模型应用于导弹备件消耗预测的构想。首先阐述了粗糙集、信息熵、自适应粒子群优化算法和加权最小二乘支持机的基本原理,并改进了自适应粒子群优化算法的搜索方式和最小二乘支持向量机的加权方法;然后建立了基于粗糙集、熵权法和自适应粒子群优化加权最小二乘支持向量机的导弹备件消耗预测模型,并分析了其实现过程。实例结果表明,所建立的组合预测模型在进行导弹备件消耗预测时具有较高的精度和重要的实用价值。

Consumption forecasting of Missile spare parts based on improved least squares support vector machine

On the basis of analyzing systemically present research condition of forecast method toward weapon and equipment spare parts and consumption characteristic of Missile spare parts,the paper brought forward the thought of applying forecast model composed of rough set、entropy weight 、and weighted least squares support vector machine with adaptive particle swarm optimization to consumption forecasting of Missile spare parts.Firstly,the paper presented basic theory,improved on search mode of adaptive particle swarm optimization and weighted method of least squares support vector machine;Secondly,the paper established consumption forecasting model of Missile spare parts based on rough set、entropy weight and weighted least squares support vector machine with adaptive particle swarm optimization,and analyzed its realization process.Lastly,the example results proved the combinatorial forecasting model have better forecast precision and important applied value in the course of consumption forecasting of Missile spare parts.