{"title":"基于相空间重构的神经网络与确定性混沌理论相结合的日降雨径流预报","authors":"M. Chettih, Khaled Chorfi, K. Mouattah","doi":"10.1109/ISPS.2015.7244983","DOIUrl":null,"url":null,"abstract":"Chaotic analysis of hydrological series revealed the presence of chaotic structures. As such, a Chaotic Neural Network model was proposed for daily rainfall-runoff. The approach is based on the combination of the series generated by the reconstruction of the phase space according to the method of Takens, in an artificial neural network. The results are very encouraging and open the prospects for other intelligent hybrid models taking into account the long dependency and multiscale effect optimized by genetic algorithms.","PeriodicalId":165465,"journal":{"name":"2015 12th International Symposium on Programming and Systems (ISPS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Combining a neural network with deterministic chaos theory using phase space reconstruction for daily rainfall-runoff forecasting\",\"authors\":\"M. Chettih, Khaled Chorfi, K. Mouattah\",\"doi\":\"10.1109/ISPS.2015.7244983\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Chaotic analysis of hydrological series revealed the presence of chaotic structures. As such, a Chaotic Neural Network model was proposed for daily rainfall-runoff. The approach is based on the combination of the series generated by the reconstruction of the phase space according to the method of Takens, in an artificial neural network. The results are very encouraging and open the prospects for other intelligent hybrid models taking into account the long dependency and multiscale effect optimized by genetic algorithms.\",\"PeriodicalId\":165465,\"journal\":{\"name\":\"2015 12th International Symposium on Programming and Systems (ISPS)\",\"volume\":\"45 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-04-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 12th International Symposium on Programming and Systems (ISPS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISPS.2015.7244983\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 12th International Symposium on Programming and Systems (ISPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISPS.2015.7244983","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Combining a neural network with deterministic chaos theory using phase space reconstruction for daily rainfall-runoff forecasting
Chaotic analysis of hydrological series revealed the presence of chaotic structures. As such, a Chaotic Neural Network model was proposed for daily rainfall-runoff. The approach is based on the combination of the series generated by the reconstruction of the phase space according to the method of Takens, in an artificial neural network. The results are very encouraging and open the prospects for other intelligent hybrid models taking into account the long dependency and multiscale effect optimized by genetic algorithms.