The nature of statistical learning theory
The nature of statistical learning theory
Uncertainty, fuzzy logic, and signal processing
Signal Processing - Special issue on fuzzy logic in signal processing
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
A fast learning algorithm for parismonious fuzzy neural systems
Fuzzy Sets and Systems - Information processing
Pattern recognition using type-II fuzzy sets
Information Sciences—Informatics and Computer Science: An International Journal
Classification-based objective functions
Machine Learning
Experimental study of intelligent controllers under uncertainty using type-1 and type-2 fuzzy logic
Information Sciences: an International Journal
Fast learning in networks of locally-tuned processing units
Neural Computation
Type-2 Fuzzy Logic: A Historical View
IEEE Computational Intelligence Magazine
Rule base reduction: some comments on the use of orthogonal transforms
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Modeling uncertainty in clinical diagnosis using fuzzy logic
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Comments on “Dynamical Optimal Training for Interval Type-2 Fuzzy Neural Network (T2FNN)”
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Computing derivatives in interval type-2 fuzzy logic systems
IEEE Transactions on Fuzzy Systems
A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots
IEEE Transactions on Fuzzy Systems
Type-2 fuzzy hidden Markov models and their application to speech recognition
IEEE Transactions on Fuzzy Systems
Interval Type-2 Fuzzy Logic Systems Made Simple
IEEE Transactions on Fuzzy Systems
Geometric Type-1 and Type-2 Fuzzy Logic Systems
IEEE Transactions on Fuzzy Systems
Uncertain Fuzzy Reasoning: A Case Study in Modelling Expert Decision Making
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A Fast Geometric Method for Defuzzification of Type-2 Fuzzy Sets
IEEE Transactions on Fuzzy Systems
A survey-based type-2 fuzzy logic system for energy management in hybrid electrical vehicles
Information Sciences: an International Journal
Information Sciences: an International Journal
Advances in Fuzzy Systems
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Type-2 fuzzy systems are increasing in popularity, and there are many examples of successful applications. While many techniques have been proposed for creating parsimonious type-1 fuzzy systems, there is a lack of such techniques for type-2 systems. The essential problem is to reduce the number of rules, while maintaining the system's approximation performance. In this paper, four novel indexes for ranking the relative contribution of type-2 fuzzy rules are proposed, which are termed R-values, c-values, ω1 -values, and ω2 -values. The R-values of type-2 fuzzy rules are obtained by applying a QR decomposition pivoting algorithm to the firing strength matrices of the trained fuzzy model. The c-values rank rules based on the effects of rule consequents, while the ω1 -values and ω2 -values consider both the rule-base structure (via firing strength matrices) and the output contribution of fuzzy rule consequents. Two procedures for utilizing these indexes in fuzzy rule selection (termed "forward selection" and "backward elimination") are described. Experiments are presented which demonstrate that by using the proposed methodology, the most influential type-2 fuzzy rules can be effectively retained in order to construct parsimonious type-2 fuzzy models.