A Method Based on Extended Fuzzy Transforms to Approximate Fuzzy Numbers in Mamdani Fuzzy Rule-Based System

<div class="floats-full-page floats-full-page-other"><h2>Table 5</h2><div class="floats-full-page-caption"><div>Comparisons obtained approximating input and output FN with trapezoidal FN.</div></div><div class="floats-full-page-content"><table class="fixed-width table-group sidewaystable" id="tab5"><tr><td><table class="table"><colgroup><col style="width:54.27em" /><col style="width:4.49em" /></colgroup><tr><td class="thead-hr" colspan="2"><hr /></td></tr><tr class="thead"><td class="align_left"><b>Comparison parameter</b></td><td class="align_center"><b>Value</b></td></tr><tr><td class="thead-hr" colspan="2"><hr /></td></tr><tr><td class="align_left"><b>Mean RMSE index for the fuzzy sets approximation with trapezoidal FNs</b></td><td class="align_center">6.3×10<sup>-2</sup></td></tr><tr><td class="align_left"><b>Mean difference of the final crisp livability values compared with the ones obtained by using the extended iF-transform method</b></td><td class="align_center">5.58%</td></tr><tr><td class="align_left"><b>Number of municipalities classified with different linguistic labels</b></td><td class="align_center">7</td></tr><tr><td class="align_left"><b>Percentage of municipalities classified with different linguistic labels</b></td><td class="align_center">7.61%</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr" /></td></tr></table></td></tr></table></div></div>

Table 5 | A Method Based on Extended Fuzzy Transforms to Approximate Fuzzy Numbers in Mamdani Fuzzy Rule-Based System

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Table 5: A Method Based on Extended Fuzzy Transforms to Approximate Fuzzy Numbers in Mamdani Fuzzy Rule-Based System

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Table 5

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<p><em>Advances in Fuzzy Systems</em> provides an international forum for original research articles in the theory and applications of fuzzy subsets and systems.</p>


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<p>Chief Editor, Professor Melin, is a professor at the Tijuana Institute of Technology. Her research interests include modular neural networks, type-2 fuzzy logic, pattern recognition, fuzzy control, neuro-fuzzy and genetic-fuzzy hybrid approaches.</p>


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