國立高雄大學圖資館 |

Language: English

Back

Machine learning for model order red...

Mohamed, Khaled Salah.

Machine learning for model order reduction

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning for model order reductionby Khaled Salah Mohamed.
Author:	Mohamed, Khaled Salah.
Published:	Cham :Springer International Publishing :2018.
Description:	xi, 93 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	http://dx.doi.org/10.1007/978-3-319-75714-8
ISBN:	9783319757148$q(electronic bk.)

Machine learning for model order reduction
Mohamed, Khaled Salah.

Machine learning for model order reduction[electronic resource] /by Khaled Salah Mohamed. - Cham :Springer International Publishing :2018. - xi, 93 p. :ill. (some col.), digital ;24 cm.

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. 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.

ISBN: 9783319757148$q(electronic bk.)

Standard No.: 10.1007/978-3-319-75714-8doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .M643 2018

Dewey Class. No.: 006.31

Machine learning for model order reduction
LDR:02586nmm a2200313 a 4500 001 533828
003 DE-He213
005 20180913113231.0
006 m d
007 cr nn 008maaau
008 181205s2018 gw s 0 eng d
020 $a 9783319757148$q(electronic bk.)
020 $a 9783319757131$q(paper)
024 7 $a 10.1007/978-3-319-75714-8 $2 doi
035 $a 978-3-319-75714-8
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .M643 2018
072 7 $a TJFC $2 bicssc
072 7 $a TEC008010 $2 bisacsh
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .M149 2018
100 1 $a Mohamed, Khaled Salah. $3 736893
245 1 0 $a Machine learning for model order reduction $h [electronic resource] / $c by Khaled Salah Mohamed.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a xi, 93 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Machine learning. $3 188639
650 0 $a Integrated circuits $x Very large scale integration $x Design and construction. $3 182089
650 1 4 $a Engineering. $3 210888
650 2 4 $a Circuits and Systems. $3 274416
650 2 4 $a Processor Architectures. $3 274498
650 2 4 $a Electronics and Microelectronics, Instrumentation. $3 274412
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-75714-8
950 $a Engineering (Springer-11647)