Noise reduction in state space using the focused gamma neural network
José Carlos Príncipe, Jyh-Ming Kuo · 2002
In this paper we utilize the gamma neural model to improve the signal to noise ratio (SNR) of broadband signals corrupted by white noise. The projection of a noisy signal onto the signal subspace can not remove the noise in the subspace. A focus gamma network, when trained as a nonlinear predictor of the projected trajectory, reduces this noise further. The property of adaptive memory depth of the gamma model is utilized to decide when to stop the training of the network. The preliminary results show that the SNR can be improved significantly, preserving the broadband signal spectrum.>