کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11023861 | 1701243 | 2019 | 35 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Modeling of multiple-input, time-varying systems with recursively estimated basis expansions
ترجمه فارسی عنوان
مدل سازی چندین ورودی، سیستم های متغیر زمان با بسط مجدد بنیاد بازگشتی
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کلمات کلیدی
DCAKFMNMSEBICVAFAICVVSSMESNRPetCO2RLSCBFVRCTDLFMABPGenetic algorithm - الگوریتم ژنتیکDynamic cerebral autoregulation - تنظیم خودکار مغز دینامیکRecursive least squares - حداقل مربعات مجازTime-varying - زمان متغیرcerebral blood flow velocity - سرعت جریان خون مغزیVasovagal syncope - سنکوپ VasovagalTime-varying systems - سیستم های مختلف زمانHead-up tilt - شیب بلند کردنForgetting factor - عامل فراموش کردنmean arterial blood pressure - فشار خون شریانیKalman filter - فیلتر کالمان یا فیلتر کالمنAdaptive Kalman filter - فیلتر کالمن مناسبBayesian information criterion - معیار اطلاعات بیزیAkaike information criterion - معیار اطلاعاتی آکائیکsignal to noise ratio - نسبت سیگنال به نویزHUT - هات
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
چکیده انگلیسی
We present novel computational schemes for estimating single- (SI) and multiple-input (MI) time-varying (TV) systems, combining a Laguerre-Volterra model formulation with improved recursive schemes based on conventional Recursive Least Squares (RLS) and Kalman Filtering (KF). The proposed recursive estimators achieve superior performance, particularly in the case of TV systems with multiple-inputs or systems that exhibit mixed-mode variations. RLS-based schemes were found to perform better in the case of TV linear systems, while the KF-based schemes were found to perform considerably better in the case of TV nonlinear systems. Model order selection and tuning of the estimator hyperparameters were implemented using Genetic Algorithms (GA), significantly improving performance and reducing computation time. Furthermore, exploiting the search efficiency in hyperparameter space yielded by the proposed GA, we rigorously examined the correlations between the hyperparameter values, the model complexity and the TV characteristics of the true underlying system. The performance of the proposed TV system identification framework was assessed using simulations and experimental data from patients undergoing head-up tilt testing for the diagnosis of vasovagal syncope.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 155, February 2019, Pages 287-300
Journal: Signal Processing - Volume 155, February 2019, Pages 287-300
نویسندگان
Kyriaki Kostoglou, Ronald Schondorf, Georgios D. Mitsis,