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Evolving Rule-based Models: A Tool for Design of Flexible Adaptive Systems

Research output: Book/Report/Proceedings › Book

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Evolving Rule-based Models: A Tool for Design of Flexible Adaptive Systems. / Angelov, Plamen.
Heidelberg, Germany: Springer Verlag, 2002. 213 p. (Studies in Fuzziness and Soft Computing).

Research output: Book/Report/Proceedings › Book

Harvard

Angelov, P 2002, Evolving Rule-based Models: A Tool for Design of Flexible Adaptive Systems. Studies in Fuzziness and Soft Computing, Springer Verlag, Heidelberg, Germany. <http://www.springeronline.com/sgw/cda/frontpage/0,10735,5-0-72-2224473-0,00.html>

APA

Angelov, P. (2002). Evolving Rule-based Models: A Tool for Design of Flexible Adaptive Systems. (Studies in Fuzziness and Soft Computing). Springer Verlag. http://www.springeronline.com/sgw/cda/frontpage/0,10735,5-0-72-2224473-0,00.html

Vancouver

Angelov P. Evolving Rule-based Models: A Tool for Design of Flexible Adaptive Systems. Heidelberg, Germany: Springer Verlag, 2002. 213 p. (Studies in Fuzziness and Soft Computing).

Author

Angelov, Plamen. / Evolving Rule-based Models: A Tool for Design of Flexible Adaptive Systems. Heidelberg, Germany : Springer Verlag, 2002. 213 p. (Studies in Fuzziness and Soft Computing).

Bibtex

@book{0a9aa56403ac49b7bd4eb5ef4e0abfa3,
title = "Evolving Rule-based Models: A Tool for Design of Flexible Adaptive Systems",
abstract = "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.",
keywords = "fuzzy systems evolving on-line learning, DCS-publications-id, book-71, DCS-publications-personnel-id, 82",
author = "Plamen Angelov",
year = "2002",
month = feb,
day = "1",
language = "English",
isbn = "3-7908-1457-1",
series = "Studies in Fuzziness and Soft Computing",
publisher = "Springer Verlag",

}

RIS

TY - BOOK

T1 - Evolving Rule-based Models: A Tool for Design of Flexible Adaptive Systems

AU - Angelov, Plamen

PY - 2002/2/1

Y1 - 2002/2/1

N2 - 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.

AB - 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.

KW - fuzzy systems evolving on-line learning

KW - DCS-publications-id

KW - book-71

KW - DCS-publications-personnel-id

KW - 82

M3 - Book

SN - 3-7908-1457-1

T3 - Studies in Fuzziness and Soft Computing

BT - Evolving Rule-based Models: A Tool for Design of Flexible Adaptive Systems

PB - Springer Verlag

CY - Heidelberg, Germany

ER -