Machine Learning for Model Order Reduction (Record no. 79461)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03496nam a22005295i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-319-75714-8 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220801221242.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 180302s2018 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783319757148 |
| -- | 978-3-319-75714-8 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 621.3815 |
| 100 1# - AUTHOR NAME | |
| Author | Mohamed, Khaled Salah. |
| 245 10 - TITLE STATEMENT | |
| Title | Machine Learning for Model Order Reduction |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2018. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XI, 93 p. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 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 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-319-75714-8 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2018. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Electronic circuits. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Microprocessors. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer architecture. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Electronics. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Electronic Circuits and Systems. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Processor Architectures. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Electronics and Microelectronics, Instrumentation. |
| 912 ## - | |
| -- | ZDB-2-ENG |
| 912 ## - | |
| -- | ZDB-2-SXE |
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