文章基本信息

标题：Identification of Conductive Fiber Parameters with Transcutaneous Electrical Nerve Stimulation Signal Using RLS Algorithm
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作者：Manuel Schimmack ; Paolo Mercorelli
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2015
卷号：48
期号：20
页码：389-394
DOI：10.1016/j.ifacol.2015.10.171
语种：English
出版社：Elsevier
摘要：AbstractThis paper presents a scaled system identification method for medical application in the fields of noninvasive nerve stimulation and neurological disease treatment. The Recursive Least Squares (RLS) method with an implemented forgetting factor was used to estimate parameters, including inductance, within the nano range of a linear model, using input-output scaling factors. To estimate the parameters in the nano range, the input signal must have a very high frequency, which subsequently requires a very high sampling rate, and therefore expensive hardware and precise programming. In contrast, this technique allows for a lower sampling rate and an input signal with low frequency to identify the parameters characterizing the linear model, without requiring additional hardware for the estimation process. This method was used to provide a scaled identification bandwidth together with a reduced sampling rate to use a Transcutaneous Electrical Nerve Stimulation signal (TENS) by themself for the identification process. In addition, the proposed method identified the inductance of the conductive textile system, the most critical parameter to be estimated. The measured results indicated that the proposed RLS method along with a forgetting factor was an effective and robust method for estimating the parameters.
关键词：KeywordsTranscutaneous Electrical Nerve Stimulation SignalOutput-Error (OE) ModelParameter EstimationRecursive algorithmsRecursive Least-squares methodLeast-Squares identification