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Joint Stochastic Spline and Autoregressive Identification Aiming Order Reduction Based on Noisy Sensor Data

This article introduces the spline approximation concept, in the context of system identification, aiming to obtain useful autoregressive models of reduced order. Models with a small number of poles are extremely useful in real time control applications, since the corresponding regulators are easier...

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Autores principales: Stefanoiu, Dan, Culita, Janetta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570758/
https://www.ncbi.nlm.nih.gov/pubmed/32899822
http://dx.doi.org/10.3390/s20185038
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author Stefanoiu, Dan
Culita, Janetta
author_facet Stefanoiu, Dan
Culita, Janetta
author_sort Stefanoiu, Dan
collection PubMed
description This article introduces the spline approximation concept, in the context of system identification, aiming to obtain useful autoregressive models of reduced order. Models with a small number of poles are extremely useful in real time control applications, since the corresponding regulators are easier to design and implement. The main goal here is to compare the identification models complexity when using two types of experimental data: raw (affected by noises mainly produced by sensors) and smoothed. The smoothing of raw data is performed through a least squares optimal stochastic cubic spline model. The consecutive data points necessary to build each polynomial of spline model are adaptively selected, depending on the raw data behavior. In order to estimate the best identification model (of ARMAX class), two optimization strategies are considered: a two-step one (which provides first an optimal useful model and then an optimal noise model) and a global one (which builds the optimal useful and noise models at once). The criteria to optimize rely on the signal-to-noise ratio, estimated both for identification and validation data. Since the optimization criteria usually are irregular in nature, a metaheuristic (namely the advanced hill climbing algorithm) is employed to search for the model optimal structure. The case study described in the end of the article is concerned with a real plant with nonlinear behavior, which provides noisy acquired data. The simulation results prove that, when using smoothed data, the optimal useful models have significantly less poles than when using raw data, which justifies building cubic spline approximation models prior to autoregressive identification.
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spelling pubmed-75707582020-10-28 Joint Stochastic Spline and Autoregressive Identification Aiming Order Reduction Based on Noisy Sensor Data Stefanoiu, Dan Culita, Janetta Sensors (Basel) Article This article introduces the spline approximation concept, in the context of system identification, aiming to obtain useful autoregressive models of reduced order. Models with a small number of poles are extremely useful in real time control applications, since the corresponding regulators are easier to design and implement. The main goal here is to compare the identification models complexity when using two types of experimental data: raw (affected by noises mainly produced by sensors) and smoothed. The smoothing of raw data is performed through a least squares optimal stochastic cubic spline model. The consecutive data points necessary to build each polynomial of spline model are adaptively selected, depending on the raw data behavior. In order to estimate the best identification model (of ARMAX class), two optimization strategies are considered: a two-step one (which provides first an optimal useful model and then an optimal noise model) and a global one (which builds the optimal useful and noise models at once). The criteria to optimize rely on the signal-to-noise ratio, estimated both for identification and validation data. Since the optimization criteria usually are irregular in nature, a metaheuristic (namely the advanced hill climbing algorithm) is employed to search for the model optimal structure. The case study described in the end of the article is concerned with a real plant with nonlinear behavior, which provides noisy acquired data. The simulation results prove that, when using smoothed data, the optimal useful models have significantly less poles than when using raw data, which justifies building cubic spline approximation models prior to autoregressive identification. MDPI 2020-09-04 /pmc/articles/PMC7570758/ /pubmed/32899822 http://dx.doi.org/10.3390/s20185038 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Stefanoiu, Dan
Culita, Janetta
Joint Stochastic Spline and Autoregressive Identification Aiming Order Reduction Based on Noisy Sensor Data
title Joint Stochastic Spline and Autoregressive Identification Aiming Order Reduction Based on Noisy Sensor Data
title_full Joint Stochastic Spline and Autoregressive Identification Aiming Order Reduction Based on Noisy Sensor Data
title_fullStr Joint Stochastic Spline and Autoregressive Identification Aiming Order Reduction Based on Noisy Sensor Data
title_full_unstemmed Joint Stochastic Spline and Autoregressive Identification Aiming Order Reduction Based on Noisy Sensor Data
title_short Joint Stochastic Spline and Autoregressive Identification Aiming Order Reduction Based on Noisy Sensor Data
title_sort joint stochastic spline and autoregressive identification aiming order reduction based on noisy sensor data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570758/
https://www.ncbi.nlm.nih.gov/pubmed/32899822
http://dx.doi.org/10.3390/s20185038
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