Principles of Adaptive Filters and Self-learning Systems [electronic resource] /by Anthony Zaknich ; edited by Michael J. Grimble, Michael A. Johnson.
by Zaknich, Anthony [author.]; Grimble, Michael J [editor.]; Johnson, Michael A [editor.]; SpringerLink (Online service).
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Item type | Current location | Call number | Status | Date due | Barcode |
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TA1637-1638 (Browse shelf) | Available | ||||
TK7882.S65 (Browse shelf) | Available | ||||
Long Loan | MAIN LIBRARY | TK5102.9 (Browse shelf) | Available |
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R858-859.7 Implementing an Electronic Health Record System | QA276-280 Hebbian Learning and Negative Feedback Networks | QA276-280 Probabilistic Modeling in Bioinformatics and Medical Informatics | TA1637-1638 Principles of Adaptive Filters and Self-learning Systems | TA1637-1638 Computer Graphics and Geometric Modeling | QA273.A1-274.9 Eigenvalues, Inequalities, and Ergodic Theory | QA274-274.9 Eigenvalues, Inequalities, and Ergodic Theory |
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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