Unervised Adaptive Filtering, Blind Source SeparationSimon Haykin A complete, one-stop reference on the state of the act of unsupervised adaptive filtering While unsupervised adaptive filtering has its roots in the 1960s, more recent advances in signal processing, information theory, imaging, and remote sensing have made this a hot area for research in several diverse fields. This book brings together cutting-edge information previously available only in disparate papers and articles, presenting a thorough and integrated treatment of the two major classes of algorithms used in the field, namely, blind signal separation and blind channel equalization algorithms. Divided into two volumes for ease of presentation, this important work shows how these algorithms, although developed independently, are closely related foundations of unsupervised adaptive filtering. Through contributions by the foremost experts on the subject, the book provides an up-to-date account of research findings, explains the underlying theory, and discusses potential applications in diverse fields. More than 100 illustrations as well as case studies, appendices, and references further enhance this excellent resource. Topics in Volume I include:
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adaptive filtering algebra algorithm Amari applications approach Bell and Sejnowski blind deconvolution blind equalization blind separation blind signal separation blind source separation Bussgang Cardoso channel Chevalier Cichocki coefficients complex contrast function convergence convolutive mixtures cost function covariance criterion cumulants decorrelation defined delays denotes density diagonal distribution divergence eigenvalues estimation example Figure FIR filter Gaussian Haykin IEEE IEEE Trans Independent component analysis Infomax information-theoretic input inverse IPCs iterations Jutten kernel kurtosis learning algorithm learning rule linear mapper mapping maximization measure method minimizing mixing matrix multichannel blind mutual information natural gradient negentropy Neural Computation Neural Networks noise nonlinear number of sources optimal orthogonal parameters pdf's performance polynomial problem Proc random vector Renyi's samples second-order Section sensors Signal Processing solution source signals space spatial statistically independent statistics system manifold tion transform unsupervised unsupervised learning update whitening Wopt y₁ zero