Adaptive Gradient-Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique

Adaptive Gradient-Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique

The identification problem of multivariable controlled autoregressive systems with measurement noise in the form of the moving average process is considered in this paper. The key is to filter the input–output data using the data filtering technique and to decompose the identification model into two...

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Main Authors: Jian Pan, Hao Ma, Xiao Jiang, Wenfang Ding, Feng Ding
Format: Article
Language:English
Published: Hindawi-Wiley 2018-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2018/9598307
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spelling doaj-c2aea06fd04646269ad67d3be9502fc02020-11-24T23:07:40ZengHindawi-WileyComplexity1076-27871099-05262018-01-01201810.1155/2018/95983079598307Adaptive Gradient-Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering TechniqueJian Pan0Hao Ma1Xiao Jiang2Wenfang Ding3Feng Ding4Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan 430068, ChinaHubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan 430068, ChinaHubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan 430068, ChinaHubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan 430068, ChinaHubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan 430068, ChinaThe identification problem of multivariable controlled autoregressive systems with measurement noise in the form of the moving average process is considered in this paper. The key is to filter the input–output data using the data filtering technique and to decompose the identification model into two subidentification models. By using the negative gradient search, an adaptive data filtering-based gradient iterative (F-GI) algorithm and an F-GI with finite measurement data are proposed for identifying the parameters of multivariable controlled autoregressive moving average systems. In the numerical example, we illustrate the effectiveness of the proposed identification methods.http://dx.doi.org/10.1155/2018/9598307
collection DOAJ
language English
format Article
sources DOAJ
author Jian Pan
Hao Ma
Xiao Jiang
Wenfang Ding
Feng Ding
spellingShingle Jian Pan
Hao Ma
Xiao Jiang
Wenfang Ding
Feng Ding
Adaptive Gradient-Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique
Complexity
author_facet Jian Pan
Hao Ma
Xiao Jiang
Wenfang Ding
Feng Ding
author_sort Jian Pan
title Adaptive Gradient-Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique
title_short Adaptive Gradient-Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique
title_full Adaptive Gradient-Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique
title_fullStr Adaptive Gradient-Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique
title_full_unstemmed Adaptive Gradient-Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique
title_sort adaptive gradient-based iterative algorithm for multivariable controlled autoregressive moving average systems using the data filtering technique
publisher Hindawi-Wiley
series Complexity
issn 1076-2787
1099-0526
publishDate 2018-01-01
description The identification problem of multivariable controlled autoregressive systems with measurement noise in the form of the moving average process is considered in this paper. The key is to filter the input–output data using the data filtering technique and to decompose the identification model into two subidentification models. By using the negative gradient search, an adaptive data filtering-based gradient iterative (F-GI) algorithm and an F-GI with finite measurement data are proposed for identifying the parameters of multivariable controlled autoregressive moving average systems. In the numerical example, we illustrate the effectiveness of the proposed identification methods.
url http://dx.doi.org/10.1155/2018/9598307
work_keys_str_mv AT jianpan adaptivegradientbasediterativealgorithmformultivariablecontrolledautoregressivemovingaveragesystemsusingthedatafilteringtechnique
AT haoma adaptivegradientbasediterativealgorithmformultivariablecontrolledautoregressivemovingaveragesystemsusingthedatafilteringtechnique
AT xiaojiang adaptivegradientbasediterativealgorithmformultivariablecontrolledautoregressivemovingaveragesystemsusingthedatafilteringtechnique
AT wenfangding adaptivegradientbasediterativealgorithmformultivariablecontrolledautoregressivemovingaveragesystemsusingthedatafilteringtechnique
AT fengding adaptivegradientbasediterativealgorithmformultivariablecontrolledautoregressivemovingaveragesystemsusingthedatafilteringtechnique
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