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: Genetic Algorithms applications to optimization and system identification
  name:
    name part: Lin, Yun-Jeng
    name part: 1969-
    role:
      role term: creator
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: xx
    date issued: 1998
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    form: remote
    extent: viii, 38 leaves : illustrations ; 28 cm.
  abstract: Genetic Algorithms (GA) are very different from the traditional optimization techniques. GA is a new generation of artificial intelligence and its principles mimic the behavior of the biologic genes in the natural world. Its execution is simple and it can determine solutions in a very short time. According to these characteristics, GA is a very powerful method for optimal design and system identification. In this thesis, we will apply GA to two main topics. Chapter II is about optimal designing of journal bearings with the objective of minimizing energy dissipated through the bearings and Chapters III and IV are about identifying the unknown parameters of stiffness-damping systems and rotordynamic systems.
  note: by Yun-Jeng Lin.
  note: "Major subject: Mechanical Engineering".
  note: Vita.
  note: Also available online.
  note: M.S. Texas A&M University 1998.
  note: Includes bibliographical references: pages 35-38.
  subject:
    topic: Major mechanical engineering
  identifier: https://hdl.handle.net/1969.1/ETD-TAMU-1998-THESIS-L567
  related item:
    location:
      url: https://hdl.handle.net/1969.1/ETD-TAMU-1998-THESIS-L567
  record info:
    record content source: TXA
    record creation date: 990311
    record change date: 20220103134247.0
    record identifier: in00001469094