{"title":"Identification of time-varying systems using a two-dimensional B-spline algorithm","authors":"P. Z. Csurcsia, J. Schoukens, I. Kollár","doi":"10.1109/I2MTC.2012.6229494","DOIUrl":null,"url":null,"abstract":"This paper presents a new method which non-parametrically estimates a two dimensional impulse response function hLTV(t, τ) of slowly time-varying systems. A generalized B-spline technique is used for double smoothing: once over the different excitation times τ (which refers to the system memory) and once over the actual excitation time t (referring to the system behavior). If the change of the parameters of the observed system is sufficiently slow, with respect to the system dynamics, we will be able to 1) reduce the disturbing noise by additional smoothing 2) reduce the number of model parameters that need to be stored.","PeriodicalId":387839,"journal":{"name":"2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I2MTC.2012.6229494","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
This paper presents a new method which non-parametrically estimates a two dimensional impulse response function hLTV(t, τ) of slowly time-varying systems. A generalized B-spline technique is used for double smoothing: once over the different excitation times τ (which refers to the system memory) and once over the actual excitation time t (referring to the system behavior). If the change of the parameters of the observed system is sufficiently slow, with respect to the system dynamics, we will be able to 1) reduce the disturbing noise by additional smoothing 2) reduce the number of model parameters that need to be stored.