000			03168nam a22003975i 4500
710	2		_aSpringerLink (Online service) _0http://id.loc.gov/authorities/names/no2005046756 _1http://viaf.org/viaf/274647764/ _9106996
999			_c102779 _d102779 _x1
001			102779
003			DE-He213
005			20230102113059.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			180302s2018 gw | s |||| 0|eng d
020			_a9783319757148
024	7		_a10.1007/978-3-319-75714-8 _2doi
040			_bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2018 EB
100	1		_997289 _aMohamed, Khaled Salah
245	1	0	_aMachine Learning for Model Order Reduction _cby Khaled Salah Mohamed
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XI, 93 páginas)
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aEngineering (Springer-11647)
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	3		_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.
988			_aEBSPRINGER_2018
650		7	_2embne _aAprendizaje automático _9166090
650		7	_2embne _9669585 _aCircuitos integrados VLSI _xDiseño
776	0	8	_iEdición impresa: _z9783319757131
776	0	8	_iEdición impresa: _z9783319757155
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-75714-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2020 _dz _eu _zSI