{"title":"基于Volterra级数和时滞神经网络方法的行为建模","authors":"Jelena Misic, V. Markovic, Z. Marinković","doi":"10.1109/NEUREL.2014.7011456","DOIUrl":null,"url":null,"abstract":"Solving the non-linear distortion problems in wireless communications is often based on developing the behavioral models of non-linear components. In this paper, a non-linear Volterra model up to third order is developed by using an artificial neural network (ANN) approach. The Volterra kernels are derived from the parameters of a feed-forward time delay neural network with a suitable activation function. The Volterra model is implemented to represent the behavior of a low noise amplifier for LTE receiver.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"39 8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Behavioral modeling by Volterra series and time-delay neural network approach\",\"authors\":\"Jelena Misic, V. Markovic, Z. Marinković\",\"doi\":\"10.1109/NEUREL.2014.7011456\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Solving the non-linear distortion problems in wireless communications is often based on developing the behavioral models of non-linear components. In this paper, a non-linear Volterra model up to third order is developed by using an artificial neural network (ANN) approach. The Volterra kernels are derived from the parameters of a feed-forward time delay neural network with a suitable activation function. The Volterra model is implemented to represent the behavior of a low noise amplifier for LTE receiver.\",\"PeriodicalId\":402208,\"journal\":{\"name\":\"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)\",\"volume\":\"39 8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NEUREL.2014.7011456\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NEUREL.2014.7011456","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Behavioral modeling by Volterra series and time-delay neural network approach
Solving the non-linear distortion problems in wireless communications is often based on developing the behavioral models of non-linear components. In this paper, a non-linear Volterra model up to third order is developed by using an artificial neural network (ANN) approach. The Volterra kernels are derived from the parameters of a feed-forward time delay neural network with a suitable activation function. The Volterra model is implemented to represent the behavior of a low noise amplifier for LTE receiver.