Neural networks expand SP's horizons Export

@article{citeulike:812964, abstract = {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}, author = {Haykin, S.}, citeulike-article-id = {812964}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=487040}, journal = {Signal Processing Magazine, IEEE}, keywords = {decision-making, handoff, network-selection, networking, no-access}, number = {2}, pages = {24--49}, posted-at = {2006-08-22 19:52:43}, priority = {1}, title = {Neural networks expand SP's horizons}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=487040}, volume = {13}, year = {1996} }

TY - JOUR ID - citeulike:812964 L3 - citeulike-article-id:812964 N2 - 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 IS - 2 JF - Signal Processing Magazine, IEEE EP - 49 TI - Neural networks expand SP's horizons VL - 13 SP - 24 KW - decision-making KW - handoff KW - network-selection KW - networking KW - no-access AU - Haykin, S PY - 1996/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=487040 ER -