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Principles of Adaptive Filters and Self-learning Systems by Anthony Zaknich (Paperback, 2005)

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Adaptive Filtering. - Linear Systems and Stochastic Processes. - Optimisation and Least Squares Estimation. - Classical Filters and Spectral Analysis. - Optimum Wiener Filter. - Optimum Kalman Filter.

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This is an ideal textbook for one-semester introductory graduate or senior undergraduate courses in adaptive and self-learning systems for signal processing. The material progresses from a short introduction to adaptive systems through modelling, classical filters and spectral analysis to adaptive control theory, nonclassical adaptive systems and applications.Features:- Comprehensive review of linear and stochastic theory.- Design guide for practical application of the least squares estimation method and Kalman filters.- Study of classical adaptive systems together with neural networks, genetic algorithms and fuzzy logic systems and their combination deals with complex problems like underwater acoustic signal processing.- Tutorial problems and exercises which identify significant points and demonstrate the practical relevance of the theory.- PDF Solutions Manual available to tutors containing answers to the tutorial problems, course outlines, sample examination material and project assignments.