Probability-Based Identification of Hammerstein Systems With Asymmetric Noise Characteristics

In the existing identification algorithms, the output noise is usually modeled by using the thick-tailed Laplace and Student's t-distributions instead of the Gaussian distribution to acquire the robustness for the outliers. However, both Laplace and Student's t-distributions exhibit symmet...

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Vydáno v:	IEEE transactions on instrumentation and measurement Ročník 73; s. 1 - 11
Hlavní autoři:	Liu, Xin, Wang, Chen, Dai, Wei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Computational modeling Data mining Generalized expectation maximization (GEM) algorithm Hammerstein output error (OE) system Identification Mathematical models Noise measurement Normal distribution Outliers (statistics) Parameter estimation Parameters Process variables robust parameter estimation Robustness Robustness (mathematics) skewed and asymmetric noise Statistical analysis System identification
ISSN:	0018-9456, 1557-9662
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Abstract	In the existing identification algorithms, the output noise is usually modeled by using the thick-tailed Laplace and Student's t-distributions instead of the Gaussian distribution to acquire the robustness for the outliers. However, both Laplace and Student's t-distributions exhibit symmetric statistical properties which can lead to certain limitations. In this article, we introduce a robust identification algorithm for Hammerstein output error (OE) systems based on the shifted asymmetric Laplace (SAL) distribution in order to handle the skewed and asymmetric output noise. The skewness parameter and scale parameter of the SAL distribution act as the hyperparameters to govern the statistical property of the SAL distribution and both of them are adaptively extracted from the identification data. That means the statistical property of the SAL distribution is adaptively adjusted by the identification data quality, which could ensure the robustness of the introduced algorithm for the skewed and asymmetric noise as well as the outliers. In addition, the auxiliary model technique is used to solve the problem of unmeasurable process variables of the Hammerstein system; the key-term separation principle is used to reduce the computation complexity of the algorithm. The complete identification problem is mathematically formulated by using the generalized expectation maximization (GEM) algorithm and the estimation formulas of the model and noise parameters are derived simultaneously. Finally, the effectiveness of the algorithm introduced in this article is verified.
AbstractList	In the existing identification algorithms, the output noise is usually modeled by using the thick-tailed Laplace and Student’s t-distributions instead of the Gaussian distribution to acquire the robustness for the outliers. However, both Laplace and Student’s t-distributions exhibit symmetric statistical properties which can lead to certain limitations. In this article, we introduce a robust identification algorithm for Hammerstein output error (OE) systems based on the shifted asymmetric Laplace (SAL) distribution in order to handle the skewed and asymmetric output noise. The skewness parameter and scale parameter of the SAL distribution act as the hyperparameters to govern the statistical property of the SAL distribution and both of them are adaptively extracted from the identification data. That means the statistical property of the SAL distribution is adaptively adjusted by the identification data quality, which could ensure the robustness of the introduced algorithm for the skewed and asymmetric noise as well as the outliers. In addition, the auxiliary model technique is used to solve the problem of unmeasurable process variables of the Hammerstein system; the key-term separation principle is used to reduce the computation complexity of the algorithm. The complete identification problem is mathematically formulated by using the generalized expectation maximization (GEM) algorithm and the estimation formulas of the model and noise parameters are derived simultaneously. Finally, the effectiveness of the algorithm introduced in this article is verified.
Author	Dai, Wei Liu, Xin Wang, Chen
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Cites_doi	10.1109/TSMC.2017.2689920 10.1109/TSMC.2021.3071137 10.1016/j.camwa.2010.02.030 10.1007/s00034-021-01801-x 10.1109/TIE.2021.3071679 10.1109/TCST.2019.2947868 10.1002/cjce.20113 10.1109/TAC.2022.3180810 10.1109/TII.2017.2702754 10.1109/TIM.2021.3067242 10.1109/LCOMM.2021.3133018 10.1007/s00034-022-02112-5 10.1016/j.automatica.2010.10.013 10.1109/TIE.2020.3026286 10.1109/TIE.2017.2733465 10.1016/j.jfranklin.2020.03.027 10.1109/TSMC.2019.2945500 10.1109/TSMC.2019.2943882 10.1109/TAC.2022.3171389 10.1002/rnc.5468 10.1002/rnc.4961 10.1109/LCSYS.2022.3184275 10.1109/TIE.2020.3028806 10.1109/TII.2019.2931792 10.1016/j.dsp.2022.103595 10.1016/j.ymssp.2018.12.027 10.1109/TSMC.2019.2949087 10.1109/TPAMI.2013.216 10.1016/j.jfranklin.2018.01.052 10.1109/TSP.2018.2865434 10.1109/TAC.2020.3007569 10.1002/acs.3604 10.1016/j.sysconle.2006.10.026 10.1109/TPWRS.2021.3131639 10.1109/TCST.2021.3111107 10.1002/acs.2752 10.1109/TAC.2021.3131433 10.1016/j.jfranklin.2021.04.006 10.1007/978-1-4612-0173-1 10.1109/LSP.2017.2720542 10.1109/TVT.2015.2396640 10.1109/tim.2022.3155778 10.1002/rnc.6657 10.1109/ICCCBDA.2018.8386559 10.1002/rnc.5675 10.1016/j.patrec.2015.02.011 10.1002/acs.3257 10.1109/TIE.2018.2826450 10.1109/TIM.2020.3009354 10.1109/TSP.2022.3180195 10.1016/j.mcm.2010.03.002 10.1109/TEC.2021.3093010 10.1109/TIM.2021.3132913
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Snippet	In the existing identification algorithms, the output noise is usually modeled by using the thick-tailed Laplace and Student's t-distributions instead of the... In the existing identification algorithms, the output noise is usually modeled by using the thick-tailed Laplace and Student’s t-distributions instead of the...
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SubjectTerms	Algorithms Computational modeling Data mining Generalized expectation maximization (GEM) algorithm Hammerstein output error (OE) system Identification Mathematical models Noise measurement Normal distribution Outliers (statistics) Parameter estimation Parameters Process variables robust parameter estimation Robustness Robustness (mathematics) skewed and asymmetric noise Statistical analysis System identification
Title	Probability-Based Identification of Hammerstein Systems With Asymmetric Noise Characteristics
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