Neural networks expand SP's horizons

Advanced algorithms for signal processing simultaneously account for nonlinearity, nonstationarity, and non-Gaussianity. The article examines the use of neural networks as an engineering tool for signal processing applications. The aim is three fold: to articulate a new philosophy in the approach to statistical signal processing using neural networks, which (either by themselves or in combination with other suitable techniques) account for the practical realities of nonlinearity, nonstationarity, and non-Gaussianity; to describe three case studies using real-life data, which clearly demonstrate the superiority of this new approach over the classical approaches to statistical signal processing; and to discuss mutual information as a criterion for designing unsupervised neural networks, thus moving away from the mean-square error criterion.