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Comparison between Choquet and Sugeno integrals as aggregation operators for pattern recognition.

, , , , , and . NAFIPS, page 1-6. IEEE, (2016)

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General Type-2 fuzzy edge detectors applied to face recognition systems., , , , and . NAFIPS, page 1-6. IEEE, (2016)Response integration in modular neural networks using Choquet Integral with Interval type 2 Sugeno measures., , , , , and . NAFIPS/WConSC, page 1-6. IEEE, (2015)Choquet Integral with Interval Type 2 Sugeno Measures as an Integration Method for Modular Neural Networks., , , , and . WCSC, volume 342 of Studies in Fuzziness and Soft Computing, page 71-86. Springer, (2014)General type-2 fuzzy edge detector applied on face recognition system using neural networks., , , and . FUZZ-IEEE, page 2325-2330. IEEE, (2016)Type-2 Fuzzy Logic for Improving Training Data and Response Integration in Modular Neural Networks for Image Recognition., , , and . IFSA (1), volume 4529 of Lecture Notes in Computer Science, page 604-612. Springer, (2007)Fuzzy operators for quality evaluation in images edge detection., , , and . IFSA/NAFIPS, page 1182-1187. IEEE, (2013)Comparison between Choquet and Sugeno integrals as aggregation operators for pattern recognition., , , , , and . NAFIPS, page 1-6. IEEE, (2016)Edge detection method based on Interval type-2 fuzzy systems for color images., , , , , and . NAFIPS/WConSC, page 1-6. IEEE, (2015)An Intelligent System for Pattern Recognition and Time Series Prediction using Modular Neural Networks., , , , and . IJCNN, page 4071-4077. IEEE, (2006)Application of interval type-2 fuzzy logic for estimating module relevance in Sugeno integration of modular neural networks., , and . FUZZ-IEEE, page 2120-2125. IEEE, (2009)