{"title":"结合发电机模型的人工神经网络预报月黑子数:与现代方法的比较","authors":"N. Safiullin, S. Porshnev, N. Kleeorin","doi":"10.1109/USBEREIT.2018.8384584","DOIUrl":null,"url":null,"abstract":"In this paper we propose a novel method for a monthly forecast of the total sunspot number time series, based on the combination of a dynamo model with an artificial neural network. The nonlinear autoregressive scheme is used with exo-genous input, consisting of two parts: the prior real observations and the corresponding model estimations at the same time-point. The results of the monthly forecast have been compared to all the modern sunspot forecasting methods, including data assimilation techniques, showing the higher accuracy of the proposed method when using one-step prediction and monthly corrections.","PeriodicalId":176222,"journal":{"name":"2018 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Monthly sunspot numbers forecast with artificial neural network combined with dynamo model: Comparison with modern methods\",\"authors\":\"N. Safiullin, S. Porshnev, N. Kleeorin\",\"doi\":\"10.1109/USBEREIT.2018.8384584\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we propose a novel method for a monthly forecast of the total sunspot number time series, based on the combination of a dynamo model with an artificial neural network. The nonlinear autoregressive scheme is used with exo-genous input, consisting of two parts: the prior real observations and the corresponding model estimations at the same time-point. The results of the monthly forecast have been compared to all the modern sunspot forecasting methods, including data assimilation techniques, showing the higher accuracy of the proposed method when using one-step prediction and monthly corrections.\",\"PeriodicalId\":176222,\"journal\":{\"name\":\"2018 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/USBEREIT.2018.8384584\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/USBEREIT.2018.8384584","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Monthly sunspot numbers forecast with artificial neural network combined with dynamo model: Comparison with modern methods
In this paper we propose a novel method for a monthly forecast of the total sunspot number time series, based on the combination of a dynamo model with an artificial neural network. The nonlinear autoregressive scheme is used with exo-genous input, consisting of two parts: the prior real observations and the corresponding model estimations at the same time-point. The results of the monthly forecast have been compared to all the modern sunspot forecasting methods, including data assimilation techniques, showing the higher accuracy of the proposed method when using one-step prediction and monthly corrections.