System identification and noise cancellation via neural-net computing

We report on highly favorable results obtained in use of neural-net computing in the learning of processes and in the cancellation of noise in signals obscured by noise. In the first instance, we demonstrate the ability to accurately learn models of linear and nonlinear functional mappings in noisy environments. In the case of noise cancellation, we report on the ability to extract a signal from noisy background.<
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