A new book is to be published in January by Springer, Physica-Verlag,
Angelov, P.P., Evolving Rule-Based Models: A Tool for Design of Flexible
Adaptive Systems'
(http://www.springer.de/cgi-bin/search_book.pl?isbn=3-7908-1457-1)
2002. XIV, 213 pp. 106 figs., 9 tabs. Hardcover 3-7908-1457-1
Recommended Retail Price: DM 109,90 (~£35)
'A monograph written for researchers and practitioners in computational
intelligence and engineering'.
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: Evolving Rule-Based Models, Flexible Adaptive Systems, Online
Model Identification, Intelligent Control
Contents:
1.Introduction.
Part I: System Modelling: Basic Principles:
2.Conventional Methods
3.Flexible Models.
Part II: Flexible Models Identification:
4. Non-linear Approach to (Off-line) Identification of Flexible Models
5. Quasi-linear Approach to FRB Models (Off-line) Identification
6. Intelligent and Smart Adaptive Systems
7. On-line Identification of Flexible TSK-type Models
Part III: Engineering Applications:
8. Modelling Indoor Climate Control Systems
9. On-line Modelling of Fermentation Processes
10. Intelligent Risk Assessment
11. Conclusions.
The book gives both easy-to-follow introduction to System Modelling and
Fuzzy Identification and original contributions in direct non-linear
off-line and on-line identification of fuzzy rule-based models. Illustrated
with a number of practical examples from air-conditioning simulation,
biotech processes modelling and risk assessment it makes a kind of a bridge
between 'pure' mathematically-oriented theoretical texts and the needs of
the real engineering practice.
________________________________________
Dr. P P Angelov, Research Fellow
Dept of Civil & Build Eng, Loughborough University
Loughborough, Leicestershire LE11 3TU, UK
phone: +44 (1509) 223774; fax: +44 (1509) 223981
e-mail: [log in to unmask]
URL: http://www-staff.lboro.ac.uk/~cvppa
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