Noise reduction in state space using the focused gamma neural network

Jose C. Principe; Jyh-Ming Kuo

doi:10.1117/12.162686

18 November 1993 Noise reduction in state space using the focused gamma neural network

Jose C. Principe, Jyh-Ming Kuo

Proceedings Volume 2038, Chaos in Communications; (1993) https://doi.org/10.1117/12.162686
Event: SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, 1993, San Diego, CA, United States

Abstract

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 cannot remove the noise in the subspace. A focus gamma network, when trained as a non-linear 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.

Citation Download Citation

Jose C. Principe and Jyh-Ming Kuo "Noise reduction in state space using the focused gamma neural network", Proc. SPIE 2038, Chaos in Communications, (18 November 1993); https://doi.org/10.1117/12.162686

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