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Machine Learning for Model Order Reduction

 eBook
Sofort lieferbar | Lieferzeit:3-5 Tage I
ISBN-13:
9783319757148
Einband:
eBook
Seiten:
93
Autor:
Khaled Salah Mohamed
eBook Typ:
PDF
eBook Format:
PDF
Kopierschutz:
1 - PDF Watermark
Sprache:
Englisch
Beschreibung:

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.
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.