Neural Networks Modeling and Control
Applications for Unknown Nonlinear Delayed Systems in Discrete Time
Neural Networks Modelling and Control: Applications for Unknown Nonlinear Delayed Systems in Discrete Time focuses on modeling and control of discrete-time unknown nonlinear delayed systems under uncertainties based on Artificial Neural Networks. First, a Recurrent High Order Neural Network (RHONN) is used to identify discrete-time unknown nonlinear delayed systems under uncertainties, then a RHONN is used to design neural observers for the same class of systems. Therefore, both neural models are used to synthesize controllers for trajectory tracking based on two methodologies: sliding mode control and Inverse Optimal Neural Control. As well…
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Produktdetails
Weitere Autoren: Alanis, Alma Y. / Arana-Daniel, Nancy / Lopez-Franco, Carlos
- ISBN: 978-0-12-817079-3
- EAN: 9780128170793
- Produktnummer: 35966573
- Verlag: Elsevier Science & Techn.
- Sprache: Englisch
- Erscheinungsjahr: 2020
- Seitenangabe: 158 S.
- Plattform: EPUB
- Masse: 25'604 KB
- Abbildungen: Approx. 240 illustrations
Über den Autor
Jorge D. Rios, was born in Guadalajara, Jalisco, Mexico, in 1985. He received the B.Sc. degree in Computer Engineering, in 2009, the M.Sc. and Ph. D. degrees in Electronics and Computer Engineering, in 2014 and 2017, respectively, from University of Guadalajara. He is in a Postdoctoral position at University of Guadalajara. His research interests center on neural control, nonlinear time-delay systems and their applications to electrical machines and robotics.
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