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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Bibliographic Details
Published in:	IEEE transactions on instrumentation and measurement Vol. 73; pp. 1 - 11
Main Authors:	Liu, Xin, Wang, Chen, Dai, Wei
Format:	Journal Article
Language:	English
Published:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
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