Genetic Algorithms: Concepts and Designs with Disk
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High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Modeling Data Mining with Granular Computing
COMPSAC '01 Proceedings of the 25th International Computer Software and Applications Conference on Invigorating Software Development
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
International Journal of Intelligent Systems - Soft Computing for Modeling, Simulation, and Control of Nonlinear Dynamical Systems
Interval type-2 fuzzy logic and modular neural networks for face recognition applications
Applied Soft Computing
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
An improved method for edge detection based on interval type-2 fuzzy logic
Expert Systems with Applications: 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
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A new model of a modular neural network (MNN) using a granular approach and its optimization with hierarchical genetic algorithms is proposed in this paper. This model can be used in different areas of application, such as human recognition and time series prediction. In this paper, the proposed model is tested for human recognition based on the ear biometric measure. A benchmark database of the ear biometric measure is used to illustrate the advantages of the proposed model over existing approaches in the literature. The proposed method consists in the optimization of the design parameters of a modular neural network, such as number of modules, percentage of data for the training phase, goal error, learning algorithm, number of hidden layers and their respective number of neurons. This method also finds out the amount of and the specific data that can be used for the training phase based on the complexity of the problem.