Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Information Sciences: an International Journal
Experimental study of intelligent controllers under uncertainty using type-1 and type-2 fuzzy logic
Information Sciences: an International Journal
Uncertainty measures for interval type-2 fuzzy sets
Information Sciences: an International Journal
Design of interval type-2 fuzzy sliding-mode controller
Information Sciences: an International Journal
An efficient centroid type-reduction strategy for general type-2 fuzzy logic system
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
An improved method for edge detection based on interval type-2 fuzzy logic
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Optimization of interval type-2 fuzzy logic controllers using evolutionary algorithms
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Recent advances on machine learning and Cybernetics
Interval type-2 fuzzy logic systems: theory and design
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots
IEEE Transactions on Fuzzy Systems
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A novel learning methodology based on a hybrid mechanism for training interval singleton type-2 Takagi-Sugeno-Kang fuzzy logic systems uses recursive orthogonal least-squares to tune the type-1 consequent parameters and the steepest descent method to tune the interval type-2 antecedent parameters. The proposed hybrid-learning algorithm changes the interval type-2 model parameters adaptively to minimize some criteria function as new information becomes available and to match desired input-output data pairs. Its antecedent sets are type-2 fuzzy sets, its consequent sets are type-1 fuzzy sets, and its inputs are singleton fuzzy numbers without uncertain standard deviations. As reported in the literature, the performance indices of hybrid models have proved to be better than those of the individual training mechanisms used alone. Experiments were carried out involving the application of hybrid interval type-2 Takagi-Sugeno-Kang fuzzy logic systems for modeling and prediction of the scale-breaker entry temperature in a hot strip mill for three different types of coils. The results demonstrate how the interval type-2 fuzzy system learns from selected input-output data pairs and improves its performance as hybrid training progresses.