rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Machine Learning for Model Order Reduction
  name:
    name part: Mohamed, Khaled Salah.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2018
    edition: 1st ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XI, 93 p. online resource.
  abstract: 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.
  table of contents: 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.
  note: by Khaled Salah Mohamed.
  subject:
    topic: Electronic circuits
  subject:
    topic: Microprocessors
  subject:
    topic: Electronics
  subject:
    topic: Microelectronics
  subject:
    topic: Circuits and Systems
  subject:
    topic: Processor Architectures
  subject:
    topic: Electronics and Microelectronics, Instrumentation
  classification: TK7888.4
  classification: 621.3815
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783319757148
  identifier: https://doi.org/10.1007/978-3-319-75714-8
  record info:
    record creation date: 180302
    record change date: 20191023162220.0
    record identifier: 978-3-319-75714-8