000			03496nam a22005295i 4500
001			978-3-319-75714-8
003			DE-He213
005			20220801221242.0
007			cr nn 008mamaa
008			180302s2018 sz | s |||| 0|eng d
020			_a9783319757148 _9978-3-319-75714-8
024	7		_a10.1007/978-3-319-75714-8 _2doi
050		4	_aTK7867-7867.5
072		7	_aTJFC _2bicssc
072		7	_aTEC008010 _2bisacsh
072		7	_aTJFC _2thema
082	0	4	_a621.3815 _223
100	1		_aMohamed, Khaled Salah. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _954945
245	1	0	_aMachine Learning for Model Order Reduction _h[electronic resource] / _cby Khaled Salah Mohamed.
250			_a1st ed. 2018.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2018.
300			_aXI, 93 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aChapter1: 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			_aThis 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	_aElectronic circuits. _919581
650		0	_aMicroprocessors. _954946
650		0	_aComputer architecture. _93513
650		0	_aElectronics. _93425
650	1	4	_aElectronic Circuits and Systems. _954947
650	2	4	_aProcessor Architectures. _954948
650	2	4	_aElectronics and Microelectronics, Instrumentation. _932249
710	2		_aSpringerLink (Online service) _954949
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783319757131
776	0	8	_iPrinted edition: _z9783319757155
776	0	8	_iPrinted edition: _z9783030093075
856	4	0	_uhttps://doi.org/10.1007/978-3-319-75714-8
912			_aZDB-2-ENG
912			_aZDB-2-SXE
942			_cEBK
999			_c79461 _d79461