{"title":"基于神经网络的加载系统建模方法","authors":"A. Rovidz, P. Várlaki, G. Orban, T. Vadvari","doi":"10.1109/ICETET.2011.69","DOIUrl":null,"url":null,"abstract":"As in many fields in modern logistics the system modelling and identification play an important role. By the modelling of complex non-linear systems different model approximation approaches are utilized. The approximation methods of mathematics are widely used in theory and practice for several problems. In the framework of the paper a higher order singular value decomposition (HOSVD) based approximation approach for neural network (NN) model approximation is introduced. The approach will be detailed from the point of view of logistic systems but it may be applicable for other fields, as well. The NNs in this case stand for local models based on which a more complex parameter varying model can numerically be reconstructed and reduced using the HOSVD.","PeriodicalId":249173,"journal":{"name":"International Conference on Emerging Trends in Engineering & Technology","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Neural-Network Based Modelling Approach for Loading Systems\",\"authors\":\"A. Rovidz, P. Várlaki, G. Orban, T. Vadvari\",\"doi\":\"10.1109/ICETET.2011.69\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As in many fields in modern logistics the system modelling and identification play an important role. By the modelling of complex non-linear systems different model approximation approaches are utilized. The approximation methods of mathematics are widely used in theory and practice for several problems. In the framework of the paper a higher order singular value decomposition (HOSVD) based approximation approach for neural network (NN) model approximation is introduced. The approach will be detailed from the point of view of logistic systems but it may be applicable for other fields, as well. The NNs in this case stand for local models based on which a more complex parameter varying model can numerically be reconstructed and reduced using the HOSVD.\",\"PeriodicalId\":249173,\"journal\":{\"name\":\"International Conference on Emerging Trends in Engineering & Technology\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Emerging Trends in Engineering & Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICETET.2011.69\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Emerging Trends in Engineering & Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICETET.2011.69","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Neural-Network Based Modelling Approach for Loading Systems
As in many fields in modern logistics the system modelling and identification play an important role. By the modelling of complex non-linear systems different model approximation approaches are utilized. The approximation methods of mathematics are widely used in theory and practice for several problems. In the framework of the paper a higher order singular value decomposition (HOSVD) based approximation approach for neural network (NN) model approximation is introduced. The approach will be detailed from the point of view of logistic systems but it may be applicable for other fields, as well. The NNs in this case stand for local models based on which a more complex parameter varying model can numerically be reconstructed and reduced using the HOSVD.