Fuzzy systems applying a sparse rule base and a fuzzy rule interpolation based reasoning method are popular solutions in cases with partial knowledge of the modeled area or cases when the full coverage of the input space by rule antecedents would require too many rules. In several practical applications there is no human expert based knowledge; the fuzzy model has to be identified from sample data. This paper presents a freely available Matlab toolbox called RuleMaker that supports the automatic generation of a fuzzy model with low complexity. The implemented model identification methods are also reviewed.
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