Principles of Adaptive Filters and Self-learning Systems

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Springer Science & Business Media, Apr 25, 2005 - Computers - 386 pages
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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.

  

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Contents

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About the author (2005)

Anthony Zaknich (M'87-'00) was born in Vela Luka, Croatia, and immigrated to Australia in the 1950s. He received the B.E. (Electronics) and M.E.Sc. degrees from the University of Western Australia (UWA), Nedlands in 1974 and 1986, respectively; the B.A. and B.Sc. (Psychology) degrees from Ambassador University, Pasadena, CA, USA, both in 1978; and the Ph.D. degree from UWA in 1996.

He is currently an Adjunct Associate Professor at UWA, Centre for Intelligent Information Processing Systems (CIIPS) and also at Murdoch University, Perth Western Australia (Division of Science and Engineering). From 1990 to 1999 he held the position of Technical Manager for Industry Projects working as a Research Fellow and Lecturer at CIIPS in the Electrical and Electronics Engineering Department, UWA. His main work at CIIPS was involved with supervision, teaching, research and development related to signal processing and artificial neural networks at the undergraduate, postgraduate and professional-development levels. Previously, he was involved in the research and development of underwater control and acoustic signalling systems in private enterprise, and also in the establishment of a public company, Nautronix Ltd, producing and marketing products in these areas for the international market. He has supervised numerous Honours and ten postgraduate research projects, including three Ph.Ds. He has also authored/co-authored more than 56 refereed papers in technical journals and conference proceedings, has contributed five research book chapters, and authored two books in his areas of interest since 1988. His special research interest is related to integrated sensory-intelligent systems (ISIS): The philosophy, theory and applications of, intelligent signal processing; learning theory; self-learning systems; artificial neural networks; adaptive systems; time-frequency filters and signal analysis; time delay spectrometry; adaptive space-time-frequency signal processing; audio and Hi-Fi, and underwater acoustic communications systems.

Dr Zaknich is a Member of the Audio Engineering Society (AES). He served on the IEEE Western Australian Regional Interest Group Committee on Neural Networks at various times since 1993. In 1998 he won the 1996-98, UWA Electrical and Electronics Engineering Department's Outstanding Early Researcher Award, which is given to the best researcher below Senior Lecturer level over any three year period.