A recurrent dynamic neural network for noisy signal representation

Abstract This paper presents a new recurrent dynamic neural network approach to solve noisy signal representation and processing problems. Essentially, the neural network solves, in a systematic way, for the sets of representation coefficients required to model a given signal in terms of basis elementary signals. The network converges by seeking the minimum energy states. The perceived advantages over traditional approaches are in robustness of computation and ability to handle time-varying noisy signals.