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: Fuzzy control of industrial systems
    sub title: theory and applications
  name:
    name part: Shaw, Ian S.
    role:
      role term: creator
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: mau
    date issued: 1998
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xxiii, 192 pages) : illustrations.
  abstract: Fuzzy Control of Industrial Systems: Theory and Applications presents the basic theoretical framework of crisp and fuzzy set theory, relating these concepts to control engineering based on the analogy between the Laplace transfer function of linear systems and the fuzzy relation of a nonlinear fuzzy system. Included are generic aspects of fuzzy systems with an emphasis on the many degrees of freedom and its practical design implications, modeling and systems identification techniques based on fuzzy rules, parametrized rules and relational equations, and the principles of adaptive fuzzy and neurofuzzy systems. Practical design aspects of fuzzy controllers are covered by the detailed treatment of fuzzy and neurofuzzy software design tools with an emphasis on iterative fuzzy tuning, while novel stability limit testing methods and the definition and practical examples of the new concept of collaborative control systems are also given. In addition, case studies of successful applications in industrial automation, process control, electric power technology, electric traction, traffic engineering, wastewater treatment, manufacturing, mineral processing and automotive engineering are also presented, in order to assist industrial control systems engineers in recognizing situations when fuzzy and neurofuzzy would offer certain advantages over traditional methods, particularly in controlling highly nonlinear and time-variant plants and processes.
  table of contents: 1. What is an Intelligent System? -- 2. Modeling Plants and Processes of Control Systems -- 3. Introduction to Set Theory and Fuzzy Logic -- 4. Set Operations -- 5. Generic Structure of Fuzzy Controllers -- 6. Fuzzy Controllers -- 7. System Identification for Rule-Based Systems -- 8. Stability Analysis of Fuzzy Control System -- 9. Neurofuzzy Controllers -- 10. Practical Fuzzy Controller Development -- 11. Examples of Fuzzy Control -- 12. Fuzzy Model of Human Control Operator -- 13. Collaborative Intelligent Control Systems -- 14. Conclusions.
  note: by Ian S. Shaw.
  note: Includes bibliographical references and index.
  subject:
    topic: Intelligent control systems
  subject:
    topic: Fuzzy systems
  subject:
    topic: Fuzzy-Regelung
  subject:
    topic: Commande intelligente
  subject:
    topic: Systèmes flous
  subject:
    topic: Fuzzy systems
  subject:
    topic: Intelligent control systems
  classification: TJ217.5 .S45 1998eb
  classification: 628.9
  classification: PL 17
  classification: ZQ 5240
  related item:
    title info:
      title: Fuzzy control of industrial systems
    name:
      name part: Shaw, Ian S.
    identifier: (DLC) 98029955
    identifier: (OCoLC)39361120
  related item:
    title info:
      title: Kluwer international series in engineering and computer science ; 457
  identifier: 9781475728132 (electronic bk.)
  identifier: 1475728131 (electronic bk.)
  identifier: 0792382498
  identifier: 9780792382492
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: GW5XE
    record creation date: 140711
    record change date: 20260422204147.1
    record identifier: in00003554074
    language of cataloging:
      language term: eng