{"title":"跟踪周期为M的离散时间序列","authors":"G. Noone, Kaidi Hui, S. Howard","doi":"10.1109/ICICS.1997.652062","DOIUrl":null,"url":null,"abstract":"In the field of signal processing and communications, many time series are period M, discrete and noisy in nature. Not only do we often want to accurately estimate the M parameters of such a digital signal, we also require to learn the \"firing sequence\" of the parameters so that we can predict the next event in time. This is achieved by combining two neural nets. The first net clusters on the time difference between successive events to accurately estimate the parameters and the second net learns to predict which parameter is to be next in the sequence. Hence we are effectively able to track a period M discrete time series. This neural method, in general, requires only a few complete frames or cycles of the time series in order to converge, even for complicated sequences.","PeriodicalId":71361,"journal":{"name":"信息通信技术","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1997-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Tracking period M discrete time series\",\"authors\":\"G. Noone, Kaidi Hui, S. Howard\",\"doi\":\"10.1109/ICICS.1997.652062\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the field of signal processing and communications, many time series are period M, discrete and noisy in nature. Not only do we often want to accurately estimate the M parameters of such a digital signal, we also require to learn the \\\"firing sequence\\\" of the parameters so that we can predict the next event in time. This is achieved by combining two neural nets. The first net clusters on the time difference between successive events to accurately estimate the parameters and the second net learns to predict which parameter is to be next in the sequence. Hence we are effectively able to track a period M discrete time series. This neural method, in general, requires only a few complete frames or cycles of the time series in order to converge, even for complicated sequences.\",\"PeriodicalId\":71361,\"journal\":{\"name\":\"信息通信技术\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"信息通信技术\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICS.1997.652062\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"信息通信技术","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1109/ICICS.1997.652062","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In the field of signal processing and communications, many time series are period M, discrete and noisy in nature. Not only do we often want to accurately estimate the M parameters of such a digital signal, we also require to learn the "firing sequence" of the parameters so that we can predict the next event in time. This is achieved by combining two neural nets. The first net clusters on the time difference between successive events to accurately estimate the parameters and the second net learns to predict which parameter is to be next in the sequence. Hence we are effectively able to track a period M discrete time series. This neural method, in general, requires only a few complete frames or cycles of the time series in order to converge, even for complicated sequences.