The Construction And Application Of VMD-IBES-SVR Model

How to improve the accuracy of model prediction is an important research topic in many application fields at home and abroad.Support Vector Regression(SVR)is one of the main prediction methods at present,and the selection of reasonable parameters will have an important impact on its prediction accuracy.It has become a hot topic in the field of prediction research to optimize SVR parameters with the powerful solving ability of intelligent optimization algorithm and improve the prediction accuracy of SVR model.In order to improve the prediction accuracy of SVR,this paper proposes to optimize SVR parameters based on the condor intelligent optimization algorithm proposed in 2020,and plans to build a combined prediction model of condor intelligent optimization algorithm and SVR,in order to effectively improve the prediction accuracy of SVR.Improved Bald Eagle Search Algorithm.In order to solve the problem that the Bald Eagle Search Algorithm(BES)proposed in 2020 is prone to fall into local optimum,this paper firstly improves BES and proposes imporve Bald Eagle Search Algorithm(IBES)based on chaos optimization and adaptive reverse learning.In the12 benchmark functions,the results show that IBES jumps out of the local optimum,and the solving ability is greatly improved.Construct VMD-IBES-SVR combination prediction model.Since the prediction accuracy of SVR model depends on the choice of parameters,this paper further uses IBES to optimize SVR parameters and constructs VMD-IBES-SVR combined prediction model.The UCI classical data set--minimum daily temperature was used for prediction experiments,and the transverse and longitudinal comparative analysis was conducted.The results show that the prediction accuracy of the combined model constructed in this paper has been greatly improved.Empirical study on air quality index prediction.In this paper,the VMD-IBES-SVR combined prediction model is used to forecast the air quality index of Beijing and Shenzhen in 2020.Compared with the traditional prediction model,the results show that the combined model can predict the air quality index with higher accuracy,and is more suitable for solving the air quality index problem than the traditional model.The innovations of this paper are as follows:(1)Theoretical research,aiming at the shortcomings of the newly proposed condor intelligent optimization algorithm,the improved condor intelligent optimization algorithm is proposed;Based on the improved Bald Eagle Search Algorithm,a VMD-IBES-SVR combined prediction model was constructed,and the comparative analysis results show that the combined prediction model established has high accuracy.(2)In terms of applied research,AQI data of two cities(Shenzhen and Beijing)with different geographical locations,different climate types and different pollution conditions were selected for comparative analysis with other prediction models under the three prediction evaluation indexes.It is concluded that the VMD-IBES-SVR combined prediction model proposed in this paper has better prediction accuracy and generalization.