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
Neural network design
The magnitude of the diagonal elements in neural networks
Neural Networks
Accelerating neural network training using weight extrapolations
Neural Networks
Additive neural networks and periodic patterns
Neural Networks
Soft computing for control of non-linear dynamical systems
Soft computing for control of non-linear dynamical systems
Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns
International Journal of Computer Vision
Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing: An Evolutionary Approach for Neural Networks and Fuzzy Systems (Studies in Fuzziness and Soft Computing)
Experimental study of intelligent controllers under uncertainty using type-1 and type-2 fuzzy logic
Information Sciences: an International Journal
Locally recurrent neural networks for wind speed prediction using spatial correlation
Information Sciences: an International Journal
A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks
Information Sciences: an International Journal
Radial Basis Function network learning using localized generalization error bound
Information Sciences: an International Journal
Type-2 Fuzzy Logic: Theory and Applications
Type-2 Fuzzy Logic: Theory and Applications
Building fuzzy inference systems with a new interval type-2 fuzzy logic toolbox
Transactions on computational science I
Iris recognition with support vector machines
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Iris recognition using LVQ neural network
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Two Bayesian methods for junction classification
IEEE Transactions on Image Processing
Analysis of the back-propagation algorithm with momentum
IEEE Transactions on Neural Networks
Engineering Applications of Artificial Intelligence
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In this paper a neural network learning method with type-2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that handle weight adaptation and especially the use of fuzzy weights. In this work an ensemble neural network of three neural networks and the use of average integration to obtain the final result is presented. The proposed approach is applied to a case of time series prediction to illustrate the advantage of using type-2 fuzzy weights.