基于改进PSO-LSSVM的军用工程机械研制费用预测模型

Research Costs Forecasting Model of Military Engineering Machinery Based on Improved PSO-LSSVM

针对传统参数法对装备研制费用进行预测存在的局限性问题,采用改进粒子群算法(particle swarm optimization,PSO)对LSSVM模型进行改进,构建军用工程机械研制费用预测模型。运用2种优化策略改进粒子群算法,对种群初始化过程进行控制、克服粒子群算法易于早熟的缺点。用改进后的粒子群算法优化最小二乘支持向量机的模型参数和核参数,以获得更好的预测效果。预测结果表明:该费用预测模型运用于军用工程机械研制费用预测,明显优于传统预测模型,具有很好的预测精度和效率。

In order to solve the limitation problem of using the traditional parameter method to predict the research costs,it adopts the improved particle swarm optimization（PSO） to improve the LSSVM model,which constructing the development cost＇s forecasting model.It uses two kinds of optimization strategy to improve the PSO,which can control the population initialization process,and overcome the shortcomings that the particle swarm algorithm is easy to early maturity.It uses the improved particle swarm algorithm to optimize the model parameters and nuclear parameters of the least square support vector machine（LSSVM） in order to get better prediction effect.The prediction results show that the prediction model used in the cost military engineering machinery,is obviously superior to the traditional forecasting model.The improved prediction model has the very good prediction accuracy and efficiency.