Fuzzy control of industrial systems theory and applications Ian S. Show. The international series in engineering and computer science 457.

Shaw, Ian S.:

ISBN 10: 0792382498 / ISBN 13: 9780792382492

Edité par Boston, Kluwer [1998]., 1998

A propos de cet article

Ex-library with stamp and library-signature. GOOD condition, some traces of use. Ancien Exemplaire de bibliothèque avec signature et cachet. BON état, quelques traces d'usure. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. C 1115: (1998) 9780792382492 Sprache: Englisch Gewicht in Gramm: 550.

N° de réf. du vendeur 2503114

Détails bibliographiques

• Titre: Fuzzy control of industrial systems theory and applications Ian S. Show. The international series in engineering and computer science 457.
• Éditeur: Boston, Kluwer [1998].
• Date de parution: 1998
• Langue: anglais
• ISBN à 10 chiffres: 0792382498
• ISBN à 13 chiffres: 9780792382492
• Reliure: Hardcover
• Poids: 550 grammes
• Catalogues du vendeur:
  • SA MATHEMATIK

A propos de ce titre

Synopsis

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.