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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Vydané v:	IEEE transactions on instrumentation and measurement Ročník 73; s. 1 - 11
Hlavní autori:	Liu, Xin, Wang, Chen, Dai, Wei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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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