Principles of Adaptive Filters and Self-Learning Systems

How can a signal be processed for which there are few or no a priori data? Professor Zaknich provides an ideal textbook for one-semester introductory graduate or senior undergraduate courses in adaptive and self-learning systems for signal processing applications. Important topics are introduced and discussed sufficiently to give the reader adequate background for confident further investigation. The material is presented in a progression from a short introduction to adaptive systems...
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How can a signal be processed for which there are few or no a priori data? Professor Zaknich provides an ideal textbook for one-semester introductory graduate or senior undergraduate courses in adaptive and self-learning systems for signal processing applications. Important topics are introduced and discussed sufficiently to give the reader adequate background for confident further investigation. The material is presented in a progression from a short introduction to adaptive systems through modelling, classical filters and spectral analysis to adaptive control theory, nonclassical adaptive systems and applications. This is the first text to cover Kalman and Wiener filters, neural networks, genetic algorithms and fuzzy logic systems together in a unified treatment.