Soft combination of neural classifiers: a comparative study
Pattern Recognition Letters
Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing: An Evolutionary Approach for Neural Networks and Fuzzy Systems (Studies in Fuzziness and Soft Computing)
Modeling Decisions: Information Fusion and Aggregation Operators (Cognitive Technologies)
Modeling Decisions: Information Fusion and Aggregation Operators (Cognitive Technologies)
Building fuzzy inference systems with a new interval type-2 fuzzy logic toolbox
Transactions on computational science I
BDPCA plus LDA: a novel fast feature extraction technique for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A View of Averaging Aggregation Operators
IEEE Transactions on Fuzzy Systems
Face recognition with radial basis function (RBF) neural networks
IEEE Transactions on Neural Networks
Uncertainty Estimation Using Fuzzy Measures for Multiclass Classification
IEEE Transactions on Neural Networks
Review: Industrial applications of type-2 fuzzy sets and systems: A concise review
Computers in Industry
Design of interval type-2 fuzzy models through optimal granularity allocation
Applied Soft Computing
A review on the design and optimization of interval type-2 fuzzy controllers
Applied Soft Computing
Application of type-2 neuro-fuzzy modeling in stock price prediction
Applied Soft Computing
Information Sciences: an International Journal
Optimization of type-2 fuzzy systems based on bio-inspired methods: A concise review
Information Sciences: an International Journal
Information Sciences: an International Journal
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
Engineering Applications of Artificial Intelligence
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In this paper we present a method for response integration in multi-net neural systems using interval type-2 fuzzy logic and fuzzy integrals, with the purpose of improving the performance in the solution of problems with a great volume of information. The method can be generalized for pattern recognition and prediction problems, but in this work we show the implementation and tests of the method applied to the face recognition problem using modular neural networks. In the application we use two interval type-2 fuzzy inference systems (IT2-FIS); the first IT2-FIS was used for feature extraction in the training data, and the second one to estimate the relevance of the modules in the multi-net system. Fuzzy logic is shown to be a tool that can help improve the results of a neural system by facilitating the representation of human perceptions.