Principles of adaptive filters and self-learning systems
Anthony Zaknich
Springer London
<P>Kalman and Wiener Filters, Neural Networks, Genetic Algorithms and Fuzzy Logic Systems Together in One Text Book </P>  <P>How can a signal be processed for which there are few or no <EM>a priori</EM> data?</P>  <P>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.</P>  <P>Features:</P>  <UL>  <LI>Comprehensive review of linear and stochastic theory. </LI>  <LI>Design guide for practical application of the least squares estimation method and Kalman filters. </LI>  <LI>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. </LI>  <LI>Tutorial problems and exercises which identify the significant points and demonstrate the practical relevance of the theory. </LI>  <LI>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.</LI></UL>