Aerial Material Consumption Prediction Model Based on PSO-SVM
Aerial material consumption prediction is a prerequisite for precise management of aerial material inventory,and improving the accuracy of aerial material consumption prediction can significantly reduce inventory management cost.To solve the problems of poor prediction performance and low accuracy caused by multiple influencing factors and small sample data in aerial material consumption prediction,an aerial material consumption prediction model that combines Particle Swarm Optimization and Support Vector Machine is proposed.Firstly,the Particle Swarm Optimization is used to optimize Support Vector Machine parameter combination,and then,combined with the original data,it optimizes Support Vector Machine parameter combination to obtain the PSO-SVM aerial material consumption prediction model.The results indicate that the PSO-SVM model has good predictive performance and strong generalization ability.
aerial material consumptionParticle Swarm OptimizationSupport Vector Machineconsumption prediction
国家科技期刊平台
NSTL
万方数据
维普
