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Adaptive Computational Intelligence for Dynamical Systems

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Intelligence for Nonlinear Dynamics and Synchronisation

Part of the book series: Atlantis Computational Intelligence Systems ((ATLANTISCIS,volume 3))

Abstract

This chapter sheds light on the application of computational intelligence techniques for various modeling tasks of dynamic systems. In the traditional approach of system engineering, the modeling task is ne-shot experiment. That is, it takes place only once and using some static experimental data that has been ompiled in a previous stage. However, this approach may not be appropriate in situations where the system volves in a dynamically changing environment. The present chapter aims at highlighting the notion of online modeling which is about approximating the system’s behavior in a dynamical and continuous manner. Online odeling is relevant for situations where data arrives over time, the system’s operational mode changes or the environmental conditions change. We mainly focus on dynamic prediction, diagnostic, optimization, and identification.

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  1. Department of Informatics, University of Klagenfurt, Universitaetsstr. 65-67, Klagenfurt, 9020, Austria

    Abdelhamid Bouchachia

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Bouchachia, A. (2010). Adaptive Computational Intelligence for Dynamical Systems. In: Intelligence for Nonlinear Dynamics and Synchronisation. Atlantis Computational Intelligence Systems, vol 3. Atlantis Press. https://doi.org/10.2991/978-94-91216-30-5_1

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