Abstract : This paper proposes the combined forecasting model which study on the classic swine fever (CSF) morbidity, using the forecasting results of ARIMA and GM (1, 1) model as the inputs of the majorizing BP neural network. Analyzing the monthly data from 2000 to 2009 and the accuracy of the forecasting results is 97.379%, more accurate and more steady than traditional methods. This research provides efficient Analytical tools for animals’ diseases forecasting work, and can provide reference to other animal diseases.
Daoliang Li; Yingyi Chen. 6th Computer and Computing Technologies in Agriculture (CCTA), Oct 2012, Zhangjiajie, China. Springer, IFIP Advances in Information and Communication Technology, AICT-392 (Part I), pp.126-132, 2013, Computer and Computing Technologies in Agriculture VI. 〈10.1007/978-3-642-36124-1_16〉
https://hal.inria.fr/hal-01348090
Contributeur : Hal Ifip
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Soumis le : vendredi 22 juillet 2016 - 13:55:56
Dernière modification le : vendredi 22 juillet 2016 - 14:11:20
Document(s) archivé(s) le : dimanche 23 octobre 2016 - 12:49:21
Yi Liang, Shihong Liu. The Classic Swine Fever Morbidity Forecasting Research Based on Combined Model. Daoliang Li; Yingyi Chen. 6th Computer and Computing Technologies in Agriculture (CCTA), Oct 2012, Zhangjiajie, China. Springer, IFIP Advances in Information and Communication Technology, AICT-392 (Part I), pp.126-132, 2013, Computer and Computing Technologies in Agriculture VI. 〈10.1007/978-3-642-36124-1_16〉. 〈hal-01348090〉
