Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32759
A Cognitive Model for Frequency Signal Classification

Authors: Rui Antunes, Fernando V. Coito

Abstract:

This article presents the development of a neural network cognitive model for the classification and detection of different frequency signals. The basic structure of the implemented neural network was inspired on the perception process that humans generally make in order to visually distinguish between high and low frequency signals. It is based on the dynamic neural network concept, with delays. A special two-layer feedforward neural net structure was successfully implemented, trained and validated, to achieve minimum target error. Training confirmed that this neural net structure descents and converges to a human perception classification solution, even when far away from the target.

Keywords: Neural Networks, Signal Classification, Adaptative Filters, Cognitive Neuroscience

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1076750

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1600

References:


[1] Bruce Goldstein, "Sensation and Perception", Sixth Edition, WADSWORTH, 2002.
[2] Paulo Gil, "Redes Neuronais Artificiais na Modelação e Controlo de Sistemas Dinâmicos", Controlo Inteligente, DEE/FCT/UNL.
[3] Leslie Smith, "An Introduction to Neural Networks", Department of Computing Mathematics, Centre for Cognitive and Computational Neuroscience, University of Stirling, UK, 2003. Available: http:/www.cs.stir.ac.uk/~lss/NNIntro/InvSlides.html
[4] Howard Demuth, Mark Beale, "Neural Network Toolbox For Use with MATLAB - User-s Guide Version 3.0", The MathWorks, Inc, 1992.