Fuzzy interpolative reasoning strengthens the power of fuzzy inference by enhancing the robustness of fuzzy systems and reducing systems complexity. However, during the interpolation process, it is possible that multiple object values for a common variable are inferred which may lead to inconsistency in interpolated results. A novel approach  was recently proposed for identification and correction of defective rules in the transformations computed for interpolation, thereby removing the inconsistencies. However, the implementation of this work is limited to rule models involving triangular fuzzy variables. This paper extends the adaptive approach as presented in , by introducing trapezoidal variables in the representation and manipulation of fuzzy rule models. This significantly improves the applicability of adaptive fuzzy interpolation reasoning, as many fuzzy systems are modelled with trapezoidal (as well as triangular) variables.
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|Published - 2009