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: Modelling and Control of Dynamic Systems Using Gaussian Process Models
  name:
    name part: Kocijan, Juš.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2016
    edition: 1st ed. 2016.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 267 p. 117 illus., 17 illus. in color. online resource.
  abstract: This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of system identification and control. It emphasizes guidelines for working solutions and practical advice for their implementation rather than the theoretical background of Gaussian process (GP) models. The book demonstrates the potential of this recent development in probabilistic machine-learning methods and gives the reader an intuitive understanding of the topic. The current state of the art is treated along with possible future directions for research. Systems control design relies on mathematical models and these may be developed from measurement data. This process of system identification, when based on GP models, can play an integral part of control design in data-based control and its description as such is an essential aspect of the text. The background of GP regression is introduced first with system identification and incorporation of prior knowledge then leading into full-blown control. The book is illustrated by extensive use of examples, line drawings, and graphical presentation of computer-simulation results and plant measurements. The research results presented are applied in real-life case studies drawn from successful applications including: a gas–liquid separator control; urban-traffic signal modelling and reconstruction; and prediction of atmospheric ozone concentration. A MATLAB® toolbox, for identification and simulation of dynamic GP models is provided for download. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
  table of contents: System Identification with GP Models -- Incorporation of Prior Knowledge -- Control with GP Models -- Trends, Challenges and Research Opportunities -- Case Studies.
  note: by Juš Kocijan.
  subject:
    topic: Engineering
  subject:
    topic: Chemical engineering
  subject:
    topic: Statistics
  subject:
    topic: Control engineering
  subject:
    topic: Engineering
  subject:
    topic: Control
  subject:
    topic: Industrial Chemistry/Chemical Engineering
  subject:
    topic: Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
  classification: TJ212-225
  classification: 629.8
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Advances in Industrial Control
  identifier: 9783319210216
  identifier: http://dx.doi.org/10.1007/978-3-319-21021-6
  record info:
    record creation date: 151121
    record change date: 20151121043536.0
    record identifier: 978-3-319-21021-6