Angelov, Plamen (2002) Evolving Rule-based Models: A Tool for Design of Flexible Adaptive Systems. Studies in Fuzziness and Soft Computing . Springer Verlag. ISBN 3-7908-1457-1Full text not available from this repository.
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.
|Uncontrolled Keywords:||fuzzy systems evolving on-line learning ; DCS-publications-id ; book-71 ; DCS-publications-personnel-id ; 82|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Departments:||Faculty of Science and Technology > School of Computing & Communications|
|Deposited By:||Dr. Plamen Angelov|
|Deposited On:||14 Dec 2007 09:51|
|Last Modified:||28 Jan 2017 00:09|
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