Research Catalog
Principles of adaptive filters and self-learning systems
- Title
- Principles of adaptive filters and self-learning systems / A. Zaknich.
- Author
- Zaknich, Anthony.
- Publication
- [London] : Springer London, 2005.
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Status | Format | Access | Call Number | Item Location |
---|---|---|---|---|
Text | Request in advance | TK7872.F5 Z35 2005 | Off-site |
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Details
- Description
- xxii, 386 pages : illustrations; 23 cm.
- Summary
- "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."--BOOK JACKET.
- Series Statement
- Advanced textbooks in control and signal processing
- Uniform Title
- Advanced textbooks in control and signal processing.
- Subjects
- Bibliography (note)
- Includes bibliographical references and index.
- Contents
- 1. Adaptive filtering -- 2. Linear systems and stochastic processes -- 3. Optimisation and least square estimation -- 4. Parametric signal and system modelling -- 5. Optimum Wiener filter -- 6. Optimal Kalman filter -- 7. Power spectral density analysis -- 8. Adaptive finite impulse response filters -- 9. Frequency domain adaptive filters -- 10. Adaptive Volterra filters -- 11. Adaptive control systems -- 12. Introduction to neural networks -- 13. Introduction to fuzzy logic systems -- 14. Introduction to genetic algorithms -- 15. Applications of adaptive signal processing -- 16. Generic adaptive filter structures.
- ISBN
- 1852339845 (pbk. : alk. paper)
- LCCN
- 2005923608
- 9781852339845
- OCLC
- ocm60767398
- SCSB-5208320
- Owning Institutions
- Columbia University Libraries