A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Self-organizing maps
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Clinical web environment to assist the diagnosis of Alzheimer's disease and other dementias
AIC'04 Proceedings of the 4th WSEAS International Conference on Applied Informatics and Communications
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
HUMANN-based systems for differential diagnosis of dementia using neuropsychological tests
INES'10 Proceedings of the 14th international conference on Intelligent engineering systems
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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In this paper we present a parametric study of a hierarchical unsupervised modular adaptive neural network (HUMANN), in dealing with noise. HUMANN is a biologically plausible feedforward neural architecture which has the capacity for working in domains with noise and overlapping classes, with no priori information of the number of different classes in the data, with highly non-linear boundary class and with high dimensionality data vectors. It is appropriate for classification processes performing blind clustering. The study has been accomplished round the two most noise-dependent HUMANN parameters, λ and ρ, using synthesized databases (sinusoidal signals with Gaussian noise). We show that HUMANN is highly resistant to noise, improving the performance of different neural architectures such as ART2 and DIGNET. We also present the application of HUMANN for the identification of pollutants in the environment. Specifically it has been tested with Polychlorinated dibenzofurans (PCDFs), some of the most hazardous pollulants of the environment.