Principles of adaptive filters and self-learning systems

Description

Kalman and Wiener Filters, Neural Networks, Genetic Algorithms and Fuzzy Logic Systems Together in One Text Book 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. 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 to deal with such complex problems as underwater acoustic signal processing. Tutorial problems and exercises which identify the significant points and demonstrate the practical relevance of the theory. PDF Solutions Manual, available to tutors from springeronline.com, containing not just answers to the tutorial problems but also course outlines, sample examination material and project assignments to help in developing a teaching programme and to give ideas for practical investigations.