Machine Learning for Model Order Reduction
(Sprache: Englisch)
This Book discusses machine learning for model order reduction, which can be used in modern VLSI design to predict the behavior of an electronic circuit, via mathematical models that predict behavior. The author describes techniques to reduce...
Voraussichtlich lieferbar in 3 Tag(en)
versandkostenfrei
Buch (Kartoniert)
131.99 €
- Lastschrift, Kreditkarte, Paypal, Rechnung
- Kostenlose Rücksendung
- Ratenzahlung möglich
Produktdetails
Produktinformationen zu „Machine Learning for Model Order Reduction “
Klappentext zu „Machine Learning for Model Order Reduction “
This Book discusses machine learning for model order reduction, which can be used in modern VLSI design to predict the behavior of an electronic circuit, via mathematical models that predict behavior. The author describes techniques to reduce significantly the time required for simulations involving large-scale ordinary differential equations, which sometimes take several days or even weeks. This method is called model order reduction (MOR), which reduces the complexity of the original large system and generates a reduced-order model (ROM) to represent the original one. Readers will gain in-depth knowledge of machine learning and model order reduction concepts, the tradeoffs involved with using various algorithms, and how to apply the techniques presented to circuit simulations and numerical analysis.- Introduces machine learning algorithms at the architecture level and the algorithm levels of abstraction;
- Describes new, hybrid solutions for model order reduction;
- Presents machine learning algorithms in depth, but simply;
- Uses real, industrial applications to verify algorithms.
Inhaltsverzeichnis zu „Machine Learning for Model Order Reduction “
Chapter1: Introduction.- Chapter2: Bio-Inspired Machine Learning Algorithm: Genetic Algorithm.- Chapter3: Thermo-Inspired Machine Learning Algorithm: Simulated Annealing.- Chapter4: Nature-Inspired Machine Learning Algorithm: Particle Swarm Optimization, Artificial Bee Colony.- Chapter5: Control-Inspired Machine Learning Algorithm: Fuzzy Logic Optimization.- Chapter6: Brain-Inspired Machine Learning Algorithm: Neural Network Optimization.- Chapter7: Comparisons, Hybrid Solutions, Hardware architectures and New Directions.- Chapter8: Conclusions.Autoren-Porträt von Khaled Salah Mohamed
Khaled Salah Mohamed attended the school of engineering, Department of Electronics and Communications at Ain-Shams University from 1998 to 2003, where he received his B.Sc. degree in Electronics and Communications Engineering with distinction and honors. He received his Masters degree in Electronics from Cairo University, Egypt in 2008. He received his PhD degree in 2012. Dr. Khaled Salah is currently a Technical Lead at the Emulation division at Mentor Graphic, Egypt. Dr. Khaled Salah has published a large number of papers in in the top refereed journals and conferences. His research interests are in 3D integration, IP Modeling, and SoC design.
Bibliographische Angaben
- Autor: Khaled Salah Mohamed
- 2019, Softcover reprint of the original 1st ed. 2018, XI, 93 Seiten, Maße: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3030093077
- ISBN-13: 9783030093075
Sprache:
Englisch
Kommentar zu "Machine Learning for Model Order Reduction"
Schreiben Sie einen Kommentar zu "Machine Learning for Model Order Reduction".
Kommentar verfassen