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140122 ||| eng |
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|a 9783790817942
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| 100 |
1 |
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|a Angelov, Plamen P.
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| 245 |
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|a Evolving Rule-Based Models
|h Elektronische Ressource
|b A Tool for Design of Flexible Adaptive Systems
|c by Plamen P. Angelov
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| 250 |
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|a 1st ed. 2002
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| 260 |
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|a Heidelberg
|b Physica
|c 2002, 2002
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| 300 |
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|a XIII, 214 p
|b online resource
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| 505 |
0 |
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|a 1 Introduction -- I System Modelling: Basic Principles -- 2 Conventional Models -- 3 Flexible Models -- 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 -- III Engineering Applications -- 8 Modelling Indoor Climate Control Systems -- 9 On-line Modelling of Fermentation Processes -- 10 Intelligent Risk Assessment -- 11 Conclusions -- References
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| 653 |
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|a Mathematical logic
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| 653 |
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|a Applied Dynamical Systems
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Control theory
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| 653 |
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|a Systems Theory, Control
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| 653 |
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|a System theory
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| 653 |
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|a Nonlinear theories
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Mathematical Logic and Foundations
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| 653 |
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|a Dynamics
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 490 |
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|a Studies in Fuzziness and Soft Computing
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| 028 |
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|a 10.1007/978-3-7908-1794-2
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| 856 |
4 |
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|u https://doi.org/10.1007/978-3-7908-1794-2?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 511.3
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| 520 |
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|a 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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