Fuzzy Inference Models For Discrete EVent Systems
| Autor: | Laurent Capocchi, Paul Bisgambiglia, Stephane Garredu, Paul-Antoine Bisgambiglia |
|---|---|
| Přispěvatelé: | TIC, Sciences pour l'environnement (SPE), Centre National de la Recherche Scientifique (CNRS)-Université Pascal Paoli (UPP)-Centre National de la Recherche Scientifique (CNRS)-Université Pascal Paoli (UPP), Centre National de la Recherche Scientifique (CNRS)-Université Pascal Paoli (UPP) |
| Jazyk: | angličtina |
| Rok vydání: | 2010 |
| Předmět: |
0209 industrial biotechnology
Fuzzy inference DEVS Theoretical computer science Fuzzy set 02 engineering and technology Fuzzy control system [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Formalism (philosophy of mathematics) 020901 industrial engineering & automation Object-oriented modeling Modelling methods 0202 electrical engineering electronic engineering information engineering Object Class 020201 artificial intelligence & image processing Mathematics |
| Zdroj: | Fuzzy Systems (FUZZ), 2010 IEEE International Conference on Computational Intelligence 2010 IEEE World Congress on Computational Intelligence-IEEE International Conference on Fuzzy Systems (Fuzz-IEEE 2010) 2010 IEEE World Congress on Computational Intelligence-IEEE International Conference on Fuzzy Systems (Fuzz-IEEE 2010), Jul 2010, Spain. pp.1-8, ⟨10.1109/FUZZY.2010.5584707⟩ FUZZ-IEEE |
| DOI: | 10.1109/FUZZY.2010.5584707⟩ |
| Popis: | International audience; For several years, we worked to improve a discrete events modeling formalism: called DEVS. Having defined a method to take into account the inaccuracies iDEVS, in this paper, we present the second part of our research work. Generally, our approach is to associate the DEVS formalism with an object class, which allows using it to new fields of study, and in our case fuzzy systems. This paper describes a new modeling methodology. It allows to modeling and to use fuzzy inference systems (FIS) with DEVS formalism in order to perform the control or the learning on systems described incompletely or with linguistic data. The advantages of this method are numerous: to extend the DEVS formalism to other application fields; to propose new DEVS models for fuzzy inference; to provide users with simple and intuitive modeling methods. Throughout this paper we describe the tools and methods which were developed to make possible the combination of these two approaches. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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