Fuzzy Rule Interpolation and Extrapolation Techniques: Criteria and Evaluation Guidelines
Author(s) -
Domonkos Tikk,
Zsolt Csaba Johanyák,
Szilveszter Kovács,
Kok Wai Wong
Publication year - 2011
Publication title -
journal of advanced computational intelligence and intelligent informatics
Language(s) - English
Resource type - Journals
eISSN - 1343-0130
pISSN - 1883-8014
DOI - 10.20965/jaciii.2011.p0254
Subject(s) - extrapolation , computer science , interpolation (computer graphics) , fuzzy logic , data mining , fuzzy rule , set (abstract data type) , fuzzy set , bilinear interpolation , artificial intelligence , machine learning , algorithm , mathematics , statistics , programming language , motion (physics) , computer vision
This paper comprehensively analyzes Fuzzy Rule Interpolation and extrapolation Techniques (FRITs). Because extrapolation techniques are usually extensions of fuzzy rule interpolation, we treat them both as approximation techniques designed to be applied where sparse or incomplete fuzzy rule bases are used, i.e., when classical inference fails. FRITs have been investigated in the literature from aspects such as applicability to control problems, usefulness regarding complexity reduction and logic. Our objectives are to create an overall FRIT standard with a general set of criteria and to set a framework for guiding their classification and comparison. This paper is our initial investigation of FRITs. We plan to analyze details in later papers on how individual techniques satisfy the groups of criteria we propose. For analysis,MATLAB FRI Toolbox provides an easy-to-use testbed, as shown in experiments.
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