Research on Forecasting of Aviation Material Carrying Demand Based on GRA-IPSO-SVM

Abstract

Accurate prediction of aviation material requirements for off-site missions is one of the main elements of a good trip assurance,therefore,the method combining gray relation analysis(GRA),improved particle swarm optimization(IPSO)algorithm and support vector machine(SVM)is proposed for predicting aviation material.Firstly,GRA is applied to analyze the factors influencing the demand for aviation materials carrying.Secondly,the particle swarm optimization algorithm is improved by introducing activity factors and non-linear inertia coefficients,and the SVM parameters are optimized by IPSO.Finally,the optimized SVM model is used to predict the demand for aviation materials.The results show that,the root mean square error predicted by aviation material prediction method based on GRA-IPSO-SVM is decreased by 0.16 than that of by using the method based on PSO-SVM,the mean absolute percentage error is decreased by 2.18%,and the prediction time is decreased by 0.7 s.

Keywords

• aerospace materials
• gray relation analysis
• support vector machine
• improved particle swarm optimization algorithm
• demand forecasting