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010   $a3-319-75714-8
024 7 $a10.1007/978-3-319-75714-8
035   $a(CKB)4100000002892424
035   $a(MiAaPQ)EBC5341935
035   $a(DE-He213)978-3-319-75714-8
035   $a(PPN)225553791
035   $a(EXLCZ)994100000002892424
100   $a20180302d2018      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aMachine Learning for Model Order Reduction /$fby Khaled Salah Mohamed
205   $a1st ed. 2018.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (99 pages)
311   $a3-319-75713-X 
327   $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.
330   $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.
606   $aElectronic circuits
606   $aMicroprocessors
606   $aComputer architecture
606   $aElectronics
606   $aElectronic Circuits and Systems
606   $aProcessor Architectures
606   $aElectronics and Microelectronics, Instrumentation
615  0$aElectronic circuits.
615  0$aMicroprocessors.
615  0$aComputer architecture.
615  0$aElectronics.
615 14$aElectronic Circuits and Systems.
615 24$aProcessor Architectures.
615 24$aElectronics and Microelectronics, Instrumentation.
676   $a006.31
700   $aMohamed$b Khaled Salah$4aut$4http://id.loc.gov/vocabulary/relators/aut$0761899
906   $aBOOK
912   $a9910299953303321
996   $aMachine Learning for Model Order Reduction$92536973
997   $aUNINA