[PDF][PDF] Attractor learning with recurrent, artificial, nonlinear, neural network
NU Baier - Proceedings of the European Conference on Circuit …, 2003 - researchgate.net
Proceedings of the European Conference on Circuit Theory and Design, 2003•researchgate.net
A nonlinear recurrent neural network is trained to synthesize chaotic signals. The
identification process is reduced to a teaching phase and a linear regression. The influence
of the shape of the nonlinearity in the neurons and the noise amplitude are studied, as a
result some design rules can be given. In a future step we want this system to be brought to
synchronize in a way to perform signal classification.
identification process is reduced to a teaching phase and a linear regression. The influence
of the shape of the nonlinearity in the neurons and the noise amplitude are studied, as a
result some design rules can be given. In a future step we want this system to be brought to
synchronize in a way to perform signal classification.
Abstract
A nonlinear recurrent neural network is trained to synthesize chaotic signals. The identification process is reduced to a teaching phase and a linear regression. The influence of the shape of the nonlinearity in the neurons and the noise amplitude are studied, as a result some design rules can be given. In a future step we want this system to be brought to synchronize in a way to perform signal classification.
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