Statistical Learning for Systems Modeling
Seiten
2023
Our Knowledge Publishing (Verlag)
978-620-6-57567-2 (ISBN)
Our Knowledge Publishing (Verlag)
978-620-6-57567-2 (ISBN)
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The work presented in this report falls within the framework of Machine Learning where we seek to model a non-linear system and to identify online the parameters of the model considered. This model is developed in a reproducing kernel Hilbert space (RKHS). These so-called representation or black box models are linear with respect to their parameters. They have had great success in identifying nonlinear systems using kernel methods.
Okba Taouali received his Doctorate degree in Electrical Engineering in 2010 from the National School of Engineers of Monastir (ENIM). Currently, he is a lecturer at ENIM (Tunisia) and Professor at FCIT, university of Tabuk, Saudi Arabia. His research focuses on: Machine learning, kernel methods, fault diagnosis.
Erscheinungsdatum | 01.11.2023 |
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Sprache | englisch |
Maße | 152 x 229 mm |
Gewicht | 200 g |
Themenwelt | Technik ► Elektrotechnik / Energietechnik |
Schlagworte | automatic learning • core techniques • RKHS |
ISBN-10 | 620-6-57567-5 / 6206575675 |
ISBN-13 | 978-620-6-57567-2 / 9786206575672 |
Zustand | Neuware |
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