Intelligent methodologies in recursive data-based modelling

Esko K. Juuso
Control Engineering, Environmental and Chemical Engineering, Faculty of Technology, University of Oulu, Finland

Ladda ner artikelhttps://doi.org/10.3384/ecp20176466

Ingår i: Proceedings of The 61st SIMS Conference on Simulation and Modelling SIMS 2020, September 22-24, Virtual Conference, Finland

Linköping Electronic Conference Proceedings 176:66, s. 466-474

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Publicerad: 2021-03-03

ISBN: 978-91-7929-731-2

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


Intelligent methodologies are beneficial in developing understandable multimodel simulation solutions. Nonlinear scaling extends these applications by facilitating compact nonlinear approaches already at the basic level. Composite local models can continue using linear methodologies for various case-based models. The flexible handling of the new structures and recursive tuning are the keys in adapting the systems in varying operating conditions. The recursive tuning of the scaling functions has two levels: smooth adaptation and strong shape changes. Fuzzy set systems further extend application areas of the models by combining composite local models in a flexible way. The extensions of the data-based methodologies are suitable for developing these adaptive applications via the following steps: variable analysis, linear models and intelligent extensions. Evolutionary computation is used in the tuning of the resulting complex models both the scaling and interactions. Complex problems are solved level by level to keep the domain expertise as an essential part.


nonlinear systems, intelligent methods, recursive tuning, composite local models, linguistic equations


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