Adaptive gradient-based iterative algorithm for multivariable controlled autoregressive moving average systems using the data filtering technique
type: Item
instance of: scholarly article
title: Adaptive gradient-based iterative algorithm for multivariable controlled autoregressive moving average systems using the data filtering technique
zbMATH Open document ID: 1398.93348
DOI: 10.1155/2018/9598307
author: Jian Pan
author: Hao Ma
author: Xiao Jiang
author: Wenfang Ding
author: Feng Ding
published in: Complexity
publication date: 2018-08-08T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 93E12
Mathematics Subject Classification ID: 93C35
Mathematics Subject Classification ID: 93E11
zbMATH DE Number: 6914670
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/9598307
OpenAlex ID: W2883369662
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Wikidata QID: Q129412177
description: scientific article; zbMATH DE number 6914670