Principles of Adaptive Filters and Self-learning Systems
By: Zaknich, Anthony [author.].
Contributor(s): Grimble, Michael J [editor.] | Johnson, Michael A [editor.] | SpringerLink (Online service).
Material type:
BookSeries: Advanced Textbooks in Control and Signal Processing: Publisher: London : Springer London, 2005.Description: XXII, 386 p. 95 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781846281211.Subject(s): Engineering | Artificial intelligence | Mechanical engineering | Control engineering | Robotics | Mechatronics | Electrical engineering | Engineering | Signal, Image and Speech Processing | Artificial Intelligence (incl. Robotics) | Control, Robotics, Mechatronics | Electrical Engineering | Mechanical Engineering | Engineering, generalDDC classification: 621.382 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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E books
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PK Kelkar Library, IIT Kanpur | Available | EBK7504 |
Adaptive Filtering -- Linear Systems and Stochastic Processes -- Modelling -- Optimisation and Least Squares Estimation -- Parametric Signal and System Modelling -- Classical Filters and Spectral Analysis -- Optimum Wiener Filter -- Optimum Kalman Filter -- Power Spectral Density Analysis -- Adaptive Filter Theory -- Adaptive Finite Impulse Response Filters -- Frequency Domain Adaptive Filters -- Adaptive Volterra Filters -- Adaptive Control Systems -- Nonclassical Adaptive Systems -- to Neural Networks -- to Fuzzy Logic Systems -- to Genetic Algorithms -- Adaptive Filter Application -- Applications of Adaptive Signal Processing -- Generic Adaptive Filter Structures.
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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