A prediction model of the sum of container based on combined BP neural network and SVM
0
조회
0
다운로드
10
인용
0
참고문헌
키워드
초록
The prediction of the sum of container is very important in the field of container transport. Many influencingfactors can affect the prediction results. These factors are usually composed of many variables, whosecomposition is often very complex. In this paper, we use gray relational analysis to set up a proper forecastindex system for the prediction of the sum of containers in foreign trade. To address the issue of the lowaccuracy of the traditional prediction models and the problem of the difficulty of fully considering all the factorsand other issues, this paper puts forward a prediction model which is combined with a back-propagation (BP)neural networks and the support vector machine (SVM). First, it gives the prediction with the data normalizedby the BP neural network and generates a preliminary forecast data. Second, it employs SVM for the residualcorrection calculation for the results based on the preliminary data. The results of practical examples show thatthe overall relative error of the combined prediction model is no more than 1.5%, which is less than the relativeerror of the single prediction models. It is hoped that the research can provide a useful reference for theprediction of the sum of container and related studies.
Abstract
The prediction of the sum of container is very important in the field of container transport. Many influencingfactors can affect the prediction results. These factors are usually composed of many variables, whosecomposition is often very complex. In this paper, we use gray relational analysis to set up a proper forecastindex system for the prediction of the sum of containers in foreign trade. To address the issue of the lowaccuracy of the traditional prediction models and the problem of the difficulty of fully considering all the factorsand other issues, this paper puts forward a prediction model which is combined with a back-propagation (BP)neural networks and the support vector machine (SVM). First, it gives the prediction with the data normalizedby the BP neural network and generates a preliminary forecast data. Second, it employs SVM for the residualcorrection calculation for the results based on the preliminary data. The results of practical examples show thatthe overall relative error of the combined prediction model is no more than 1.5%, which is less than the relativeerror of the single prediction models. It is hoped that the research can provide a useful reference for theprediction of the sum of container and related studies.
참고문헌 (25)
- S. Makridakis, "Averages of forecasts : Some empirical results", Management Science, 29(9), pp.987~996 (1983)
- G. Chen, "Optimizing time windows for managing export container arrivals at Chinese container terminals", Maritime Economics & Logistics, 12(1), pp.111~126 (2010)
- X. Hao, "The application of combination forecasting model in forecasting the total power of agricultural machinery in Heilongjiang Province", Asian Agricultural Research, 7(5), pp.25~28 (2015)
- C. C. Chou, "A modified regression model for forecasting the volumes of Taiwan’s import containers", Mathematical and Computer Modelling, 47(9-10), pp.797~807 (2008)
- H. S. Hwang, "Container terminal demand forecasting framework using fuzzy-GMDH and neural network method", Proceedings of the 2nd International Conference on Innovative Computing, Information and Control (2007)
- W. Y. Peng, "A comparison of univariate methods for forecasting container throughput volumes", Mathematical and Computer Modelling, 50(7-8), pp.1045~1057 (2009)
- Veerachai GOSASANG, "A Comparison of Traditional and Neural Networks Forecasting Techniques for Container Throughput at Bangkok Port", The Asian Journal of Shipping and Logistics, 27(3), pp.463~482 (2011), 한국해운물류학회
- V. Gosasang, "An application of neural networks for forecasting container throughput at Bangkok port", Proceedings of the World Congress on Engineering, pp.1~5 (2010)
- J. W. Chan, "Multi-criteria material selections and end-of-life product strategy : Grey relational analysis approach", Materials & Design, 28(5), pp.1539~1546 (2007)
- F. L. Chen, "Gray relation analysis and multilayer functional link network sales forecasting model for perishable food in convenience store", Expert Systems with Applications, 36(3), pp.7054~7063 (2009)
- S. Yu, "A dynamic all parameters adaptive BP neural networks model and its application on oil reservoir prediction", Applied Mathematics and Computation, 195(1), pp.66~75 (2008)
- C. Dong, "Using BP neural networks to prioritize risk management approaches for China’s unconventional shale gas industry", Sustainability, 9 (2017)
- E. Byvatov, "Comparison of support vector machine and artificial neural network systems for drug", nondrug classification, 2003, pp.43(6), 1882~1889
- P. S. Freitas, "Model combination in neural-based forecasting", European Journal of Operational Research, 173(3), pp.801~814 (2006)
- Z. Xiao, "BP neural network with rough set for short term load forecasting", Expert Systems with Applications, 36(1), pp.273~279 (2009)
- C. C. Chang, "LIBSVM: a library for support vector machines", ACM Transactions on Intelligent Systems and Technology, 2(3) (2011)
- G. Xie, "Hybrid approaches based on LSSVR model for container throughput forecasting : a comparative study", Applied Soft Computing, 13(5), pp.2232~2241 (2013)
- P. M. Schulze, "Forecasting container transshipment in Germany", Applied Economics, 41(22), pp.2809~2815 (2009)
- 김종길, "A Study on forecasting container volume of port using SD and ARIMA", 한국항해항만학회지, 35(4), pp.343~349 (2011), 한국항해항만학회
- W. Y. Peng, "The comparison of the seasonal forecasting models: a study on the prediction of imported container volume for international container ports in Taiwan", Maritime Quarterly, 25(2), pp.21~36 (2006)
- C. I. Liu, "Design, simulation, and evaluation of automated container terminals", IEEE Transactions on Intelligent Transportation Systems, 3(1), pp.12~26 (2002)
- Raphael Diaz, "Forecasting Empty Container Volumes", The Asian Journal of Shipping and Logistics, 27(2), pp.217~236 (2011), 한국해운물류학회
- F. M. Tsai, "Using artificial neural networks to predict container flows between the major ports of Asia", International Journal of Production Research, 55(17), pp.5001~5010 (2017)
- A. Huang, "Forecasting container throughput with big data using a partially combined framework", Proceedings of the International Conference on Transportation Information and Safety (ICTIS), pp.641~646 (2015)
- Q. Wu, "Power load forecasts based on hybrid PSO with Gaussian and adaptive mutation and Wv-SVM", Expert Systems with Applications, 37(1), pp.194~201 (2010)