Derivative-Based Learning of Interval Type-2 Intuitionistic Fuzzy Logic Systems for Noisy Regression Problems

This study presents a comparative evaluation of interval type-2 intuitionistic fuzzy logic system using three derivative-based learning algorithms on noisy regression problems. The motivation for this study is to manage uncertainty in noisy regression problems for the first time using both membershi...

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Veröffentlicht in:	International journal of fuzzy systems 2020-04, Vol.22 (3), p.1007-1019
Hauptverfasser:	Eyoh, Imo Jeremiah, Umoh, Uduak Augustine, Inyang, Udoinyang Godwin, Eyoh, Jeremiah Effiong
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial Intelligence Back propagation Computational Intelligence Datasets Decision making Distance learning Engineering Extended Kalman filter Fuzzy logic Fuzzy sets Fuzzy systems Genetic algorithms Machine learning Management Science Mathematical models Neural networks Noise levels Operations Research Parameters Regression Run time (computers) Time series Uncertainty
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