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عنوان
Machine Learning for Model Order Reduction

پدید آورنده
by Khaled Salah Mohamed

موضوع
، Electronic circuits,، Electronics,، Engineering

رده
Q
325
.
M319
2018

کتابخانه
Library of Razi Metallurgical Research Center

محل استقرار
استان: Tehran ـ شهر: Tehran

Library of Razi Metallurgical Research Center

تماس با کتابخانه : 46831570-021

OTHER STANDARD IDENTIFIER

Standard Number
electronic

TITLE AND STATEMENT OF RESPONSIBILITY

Title Proper
Machine Learning for Model Order Reduction

.PUBLICATION, DISTRIBUTION, ETC

Place of Publication, Distribution, etc.
Cham
Name of Publisher, Distributor, etc.
Springer
Date of Publication, Distribution, etc.
2018

NOTES PERTAINING TO TITLE AND STATEMENT OF RESPONSIBILITY

Text of Note
by Khaled Salah Mohamed

NOTES PERTAINING TO RESPONSIBILITY

Text of Note
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

CONTENTS NOTE

Text of Note
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

TOPICAL NAME USED AS SUBJECT

Entry Element
، Electronic circuits
Entry Element
، Electronics
Entry Element
، Engineering

LIBRARY OF CONGRESS CLASSIFICATION

Class number
Q
325
.
M319
2018

PERSONAL NAME - PRIMARY RESPONSIBILITY

Relator Code
AU

AU Mohamed, Khaled Salah
AU

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