Overview
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.
Full Product Details
Author: Anthony Zaknich
Publisher: Springer
Imprint: Springer
Dimensions:
Width: 23.40cm
, Height: 2.10cm
, Length: 15.60cm
Weight: 0.576kg
ISBN: 9781848008830
ISBN 10: 184800883
Pages: 412
Publication Date: 15 September 2008
Audience:
General/trade
,
General
Format: Undefined
Publisher's Status: Unknown
Availability: Out of stock
Reviews
From the reviews: <p> An excellent tutorial for graduate students and a comprehensive introduction for researchers working in adaptive systems. Summing Up: Highly Recommended. <br>(J. Y. Cheung, Choice, February, 2006)