Type-2 fuzzy Gaussian mixture models
Pattern Recognition
Genetically Evolved Fuzzy Rule-Based Classifiers and Application to Automotive Classification
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Information Sciences: an International Journal
On constructing parsimonious type-2 fuzzy logic systems via influential rule selection
IEEE Transactions on Fuzzy Systems
α-plane representation for type-2 fuzzy sets: theory and applications
IEEE Transactions on Fuzzy Systems
An extended sliding mode learning algorithm for type-2 fuzzy neural networks
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
A fuzzy rule-based classification system using interval type-2 fuzzy sets
IUKM'11 Proceedings of the 2011 international conference on Integrated uncertainty in knowledge modelling and decision making
A survey-based type-2 fuzzy logic system for energy management in hybrid electrical vehicles
Information Sciences: an International Journal
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In this paper, we demonstrate, through the multicategory classification of battlefield ground vehicles using acoustic features, how it is straightforward to directly exploit the information inherent in a problem to determine the number of rules, and subsequently the architecture, of fuzzy logic rule-based classifiers (FLRBC). We propose three FLRBC architectures, one non-hierarchical and two hierarchical (HFLRBC), conduct experiments to evaluate the performances of these architectures, and compare them to a Bayesian classifier. Our experimental results show that: 1) for each classifier the performance in the adaptive mode that uses simple majority voting is much better than in the non-adaptive mode; 2) all FLRBCs perform substantially better than the Bayesian classifier; 3) interval type-2 (T2) FLRBCs perform better than their competing type-1 (T1) FLRBCs, although sometimes not by much; 4) the interval T2 nonhierarchical and HFLRBC-series architectures perform the best; and 5) all FLRBCs achieve higher than the acceptable 80% classification accuracy