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Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems - new book

ISBN: 9783790825060

The idea about this book has evolved during the process of its preparation as some of the results have been achieved in parallel with its writing. One reason for this is that in this area… More...

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Evolving Rule-Based Models A Tool for Design of Flexible Adaptive Systems - Angelov, Plamen P.
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Angelov, Plamen P.:

Evolving Rule-Based Models A Tool for Design of Flexible Adaptive Systems - Paperback

2010, ISBN: 3790825069

Hardcover

Softcover reprint of hardcover 1st ed. 2002 Kartoniert / Broschiert Mathematische Grundlagen, Mathematik: Logik, Mathematik für Ingenieure, Künstliche Intelligenz, algorithms; complexsy… More...

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Plamen P. Angelov:
Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems (Studies in Fuzziness and Soft Computing) - Paperback

2010

ISBN: 9783790825060

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Physica, 2010-12-15. Softcover reprint of hardcover 1. Paperback. Used:Good., Physica, 2010-12-15

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Plamen P. Angelov:
Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems (Studies in Fuzziness and Soft Computing) - Paperback

2010, ISBN: 9783790825060

Physica-Verlag HD, 2010-12-15. Paperback. Good., Physica-Verlag HD, 2010-12-15

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Evolving Rule-Based Models - Plamen P. Angelov
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Plamen P. Angelov:
Evolving Rule-Based Models - new book

ISBN: 9783790825060

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Details of the book
Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems

The objects of modelling and control change due to dynamical characteristics, fault development or simply ageing. There is a need to up-date models inheriting useful structure and parameter information. The book gives an original solution to this problem with a number of examples. It treats an original approach to on-line adaptation of rule-based models and systems described by such models. It combines the benefits of fuzzy rule-based models suitable for the description of highly complex systems with the original recursive, non iterative technique of model evolution without necessarily using genetic algorithms, thus avoiding computational burden making possible real-time industrial applications. Potential applications range from autonomous systems, on-line fault detection and diagnosis, performance analysis to evolving (self-learning) intelligent decision support systems.

Details of the book - Evolving Rule-Based Models: A Tool for Design of Flexible Adaptive Systems


EAN (ISBN-13): 9783790825060
ISBN (ISBN-10): 3790825069
Hardcover
Paperback
Publishing year: 2010
Publisher: Physica
228 Pages
Weight: 0,351 kg
Language: eng/Englisch

Book in our database since 2011-07-29T16:14:14+01:00 (London)
Detail page last modified on 2023-10-10T20:32:45+01:00 (London)
ISBN/EAN: 9783790825060

ISBN - alternate spelling:
3-7908-2506-9, 978-3-7908-2506-0
Alternate spelling and related search-keywords:
Book author: angelov
Book title: rule, systems art, flexible design


Information from Publisher

Author: Plamen P. Angelov
Title: Studies in Fuzziness and Soft Computing; Evolving Rule-Based Models - A Tool for Design of Flexible Adaptive Systems
Publisher: Physica; Physica
214 Pages
Publishing year: 2010-10-21
Heidelberg; DE
Printed / Made in
Weight: 0,355 kg
Language: English
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
POD
XIII, 214 p.

BC; Mathematical Logic and Foundations; Hardcover, Softcover / Mathematik/Grundlagen; Mathematik: Logik; Verstehen; algorithms; complex system; complex systems; decision support system; fuzzy; genetic algorithms; model; modeling; system; complexity; Systems Theory, Control; Artificial Intelligence; Complexity; Mathematical Logic and Foundations; Systems Theory, Control; Artificial Intelligence; Applied Dynamical Systems; Mathematische Grundlagen; Kybernetik und Systemtheorie; Künstliche Intelligenz; Mathematik für Ingenieure; BB

The objects of modelling and control change due to dynamical characteristics, fault development or simply ageing. There is a need to up-date models inheriting useful structure and parameter information. The book gives an original solution to this problem with a number of examples. It treats an original approach to on-line adaptation of rule-based models and systems described by such models. It combines the benefits of fuzzy rule-based models suitable for the description of highly complex systems with the original recursive, non iterative technique of model evolution without necessarily using genetic algorithms, thus avoiding computational burden making possible real-time industrial applications. Potential applications range from autonomous systems, on-line fault detection and diagnosis, performance analysis to evolving (self-learning) intelligent decision support systems.

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