A web site dedicated to Fuzzy Rule Interpolation Techniques, their implementation and application as well as model identification. It contains a comprehensive list of related papers, among which several can be downloaded, too. Several FRI and model identification techniques are implemented in the two Matlab toolboxes, which are also available for download.
http://fri.gamf.hu/
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Fuzzy Rule Interpolation Toolbox - Sparse Fuzzy Model Identification ToolBox | fri.gamf.hu Reviews
https://fri.gamf.hu
A web site dedicated to Fuzzy Rule Interpolation Techniques, their implementation and application as well as model identification. It contains a comprehensive list of related papers, among which several can be downloaded, too. Several FRI and model identification techniques are implemented in the two Matlab toolboxes, which are also available for download.
fri.gamf.hu
Fuzzy Rule Interpolation Toolbox - Sparse Fuzzy Model Identification ToolBox
http://fri.gamf.hu/links
Fuzzy Rule Interpolation and Fuzzy Model Identification. Kecskemét College, GAMF Faculty. University of Miskolc, Department of Information Technology.
Fuzzy Rule Interpolation Toolbox - Sparse Fuzzy Model Identification ToolBox
http://fri.gamf.hu/toolbox
Fuzzy Rule Interpolation and Fuzzy Model Identification. Fuzzy Rule Interpolation Matlab Toolbox. Sparse Fuzzy Model Identification Matlab ToolBox. The current version (FRIT v1.1.13) of the FRI toolbox is available for download free of charge under GNU General Public License. If you use the toolbox please refer to it as follows:. The current version (SFMI v1.1.6) of the SFMI toolbox is available for download free of charge under GNU General Public License. Unzip the files from the file SFMI ToolBox.z...
Fuzzy Rule Interpolation Toolbox - Sparse Fuzzy Model Identification ToolBox
http://fri.gamf.hu/fripapers
Fuzzy Rule Interpolation and Fuzzy Model Identification. Papers (Fuzzy Rule Interpolation, Model Identification, Practical Applications). The following are a list of papers on the background of Fuzzy Rule Interpolation and several related topics. The list is continuously growing. If you don't find your paper on it please send an e-mail to johanyak.csaba# AT# gamf.kefo.hu. Please include the BibTex description, too. Fuzzy Rule Interpolation Techniques implemented in the FRI ToolBox. Zoltan Krizsan, Szilve...
Fuzzy Rule Interpolation Toolbox - Sparse Fuzzy Model Identification ToolBox
http://fri.gamf.hu/examples
Fuzzy Rule Interpolation and Fuzzy Model Identification. The FRITs were used for the approximation of the function. Fig 1 Surface representing the relation between the inputs and outputs of the modeled phenomena. In course of the experimentation two fuzzy systems were created, each one applying a sparse rule base with six rules. The rules describe the minima and maxima of the output as well as the output corresponding to the four points bounding the input space. Experimenting the first system (trapezoida...
Fuzzy Rule Interpolation Toolbox - Sparse Fuzzy Model Identification ToolBox
http://fri.gamf.hu/sfmitoolbox
Fuzzy Rule Interpolation and Fuzzy Model Identification. Sparse Fuzzy Model Identification Matlab ToolBox. The current version (SFMI v1.1.6) of the SFMI toolbox is available for download free of charge under GNU General Public License. If you use the toolbox please refer to it as follows:. An online HTML documentation and a dependency graph of SFMI's functions is also available here. Unzip the files from the file SFMI ToolBox.zip. In the Matlab Command Window. Enjoy the program.
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Fuzzy Rule Interpolation Toolbox - Sparse Fuzzy Model Identification ToolBox
Fuzzy Rule Interpolation and Fuzzy Model Identification. Why Fuzzy Rule Interpolation is important? Fuzzy systems use fuzzy rule base to make inference. A fuzzy rule base is fully covered (at level α. If all input universes are covered by rules at level α. Such fuzzy rule bases are also called dense or complete rule bases. In practice, it means that for all the possible observations there exists at least one firing rule, whose antecedent part overlaps the input data at least partially at level α.
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