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
Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns
International Journal of Computer Vision
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern recognition using type-II fuzzy sets
Information Sciences—Informatics and Computer Science: An International Journal
International Journal of Intelligent Systems - Soft Computing for Modeling, Simulation, and Control of Nonlinear Dynamical Systems
Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing: An Evolutionary Approach for Neural Networks and Fuzzy Systems (Studies in Fuzziness and Soft Computing)
A fuzzy logic-based computational recognition-primed decision model
Information Sciences: an International Journal
A New Method for Edge Detection in Image Processing Using Interval Type-2 Fuzzy Logic
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
Is there a need for fuzzy logic?
Information Sciences: an International Journal
Information Sciences: an International Journal
Interval type-2 fuzzy membership function generation methods for pattern recognition
Information Sciences: an International Journal
Information Sciences: an International Journal
Modular neural networks with Hebbian learning rule
Neurocomputing
Interval type-2 fuzzy logic and modular neural networks for face recognition applications
Applied Soft Computing
Type-2 Fuzzy Logic: Theory and Applications
Type-2 Fuzzy Logic: Theory and Applications
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Toward a generalized theory of uncertainty (GTU)--an outline
Information Sciences: an International Journal
IEEE Transactions on Fuzzy Systems
Building fuzzy inference systems with a new interval type-2 fuzzy logic toolbox
Transactions on computational science I
Empirical analysis of an on-line adaptive system using a mixture of Bayesian networks
Information Sciences: an International Journal
An improved method for edge detection based on interval type-2 fuzzy logic
Expert Systems with Applications: An International Journal
Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization
Information Sciences: an International Journal
Fuzzy system parameters discovery by bacterial evolutionary algorithm
IEEE Transactions on Fuzzy Systems
Iris recognition based on bidimensional empirical mode decomposition and fractal dimension
Information Sciences: an International Journal
Using the idea of the sparse representation to perform coarse-to-fine face recognition
Information Sciences: an International Journal
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
Predictable type-2 fuzzy mobile units for energy balancing in wireless sensor networks
Information Sciences: an International Journal
Artificial bee colony algorithm for modular neural network
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Engineering Applications of Artificial Intelligence
Let a biogeography-based optimizer train your Multi-Layer Perceptron
Information Sciences: an International Journal
Fingerprint orientation field reconstruction by weighted discrete cosine transform
Information Sciences: an International Journal
Assessing the level of difficulty of fingerprint datasets based on relative quality measures
Information Sciences: an International Journal
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In this paper we propose a new approach to genetic optimization of modular neural networks with fuzzy response integration. The architecture of the modular neural network and the structure of the fuzzy system (for response integration) are designed using genetic algorithms. The proposed methodology is applied to the case of human recognition based on three biometric measures, namely iris, ear, and voice. Experimental results show that optimal modular neural networks can be designed with the use of genetic algorithms and as a consequence the recognition rates of such networks can be improved significantly. In the case of optimization of the fuzzy system for response integration, the genetic algorithm not only adjusts the number of membership functions and rules, but also allows the variation on the type of logic (type-1 or type-2) and the change in the inference model (switching to Mamdani model or Sugeno model). Another interesting finding of this work is that when human recognition is performed under noisy conditions, the response integrators of the modular networks constructed by the genetic algorithm are found to be optimal when using type-2 fuzzy logic. This could have been expected as there has been experimental evidence from previous works that type-2 fuzzy logic is better suited to model higher levels of uncertainty.