Intelligent Multimodel Simulation in Decomposed Systems

Esko Juuso
Control Engineering, Faculty of Technology, University of Oulu, Finland

Ladda ner artikelhttp://dx.doi.org/10.3384/ecp18153308

Ingår i: Proceedings of The 59th Conference on Simulation and Modelling (SIMS 59), 26-28 September 2018, Oslo Metropolitan University, Norway

Linköping Electronic Conference Proceedings 153:44, s. 308-315

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Publicerad: 2018-11-19

ISBN: 978-91-7685-494-5

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


Intelligent methodologies provide a good basis for multimodel simulation. Small, specialised systems have a large number of feasible solutions, but developing truly adaptive, and still understandable, systems for highly complex systems require domain expertise and more compact approaches at the basic level. The nonlinear scaling approach extends the application areas of linear methodologies to nonlinear modelling and reduces the need for decomposition with local models. Fuzzy set systems provide a good basis for understandable models for decomposed systems. Data-based methodologies are suitable for developing these adaptive applications via the following steps: variable analysis, linear models and intelligent extensions. Complex problems are solved level by level to keep the domain expertise as an essential part of the solution.


nonlinear systems, intelligent methods, composite local modelling, linguistic equations, fuzzy logic


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