Descripción del título
Evolving Rule-Based Models ...
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
Monografía
monografia Rebiun24543281 https://catalogo.rebiun.org/rebiun/record/Rebiun24543281 130215s2002 gw s 00 0 eng d 9783790817942 9783790825060 9783790814576 9783662003251 UMA.RE 519.766.2 Angelov, Plamen P. aut. http://id.loc.gov/vocabulary/relators/aut Evolving Rule-Based Models Recurso electrónico] A Tool for Design of Flexible Adaptive Systems by Plamen P. Angelov 1st ed. 2002 Heidelberg Physica-Verlag HD Imprint: Physica 2002 Heidelberg Heidelberg Physica-Verlag HD Imprint: Physica XIII, 214 p XIII, 214 p Text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Studies in fuzziness and soft computing 92 Studies in Fuzziness and Soft Computing 92 Índice Bibliogr.: p. 199-208 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 Conjuntos difusos Modelos matemáticos Logic, Symbolic and mathematical Systems theory Artificial intelligence Engineering Mathematical Logic and Foundations Systems Theory, Control Artificial Intelligence Complexity