Attractor learning with nonlinear, artificial, neural network
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.