Adaptive Filtering, Diniz

Автор: Mohinder S. Grewal,Angus P. Andrews
Название: Kalman Filtering: Theory and Practice with MATLAB
ISBN: 1118851218 ISBN-13(EAN): 9781118851210
Издательство: Wiley
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Цена: 120330.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The definitive textbook and professional reference on Kalman Filtering fully updated, revised, and expanded This book contains the latest developments in the implementation and application of Kalman filtering.

Автор: Grewal
Название: Kalman Filtering: Theory and Practice Using MATLAB, 3rd Edition
ISBN: 0470173661 ISBN-13(EAN): 9780470173664
Издательство: Wiley
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Цена: 93450.00 T
Наличие на складе: Невозможна поставка.
Описание: Organized for use as a text for an introductory course in stochastic processes at the senior level and as a first-year, graduate-level course in Kalman filtering theory and applications, this book includes real-world problems in practice as illustrative examples, and also covers the more practical aspects of implementation. The author Grewal teaches at Cal State Fullerton and also offers seminars and tutorials on Kalman Filters. Dr. Grewal has contributed the Article on Kalman Filters for the Webster Encyclopedia. 

Автор: Kong Aik Lee
Название: Subband Adaptive Filtering - Theory and Implementation
ISBN: 0470516941 ISBN-13(EAN): 9780470516942
Издательство: Wiley
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Цена: 118220.00 T
Наличие на складе: Поставка под заказ.
Описание: Subband adaptive filtering is rapidly becoming one of the most effective techniques for reducing computational complexity and improving the convergence rate of algorithms in adaptive signal processing applications. This book provides an introductory, yet extensive guide on the theory of various subband adaptive filtering techniques.

Автор: Principe Jose C
Название: Kernel Adaptive Filtering
ISBN: 0470447532 ISBN-13(EAN): 9780470447536
Издательство: Wiley
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Цена: 111880.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: * On-line learning is a fundamental tool in adaptive signalprocessing * Presents on-line learning from a signal processingperspective.

Автор: Fanbiao Li; Peng Shi; Ligang Wu
Название: Control and Filtering for Semi-Markovian Jump Systems
ISBN: 3319471988 ISBN-13(EAN): 9783319471983
Издательство: Springer
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Цена: 121110.00 T
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Описание: This book presents up-to-date research developments and novel methodologies on semi-Markovian jump systems (S-MJS). It presents solutions to a series of problems with new approaches for the control and filtering of S-MJS, including stability analysis, sliding mode control, dynamic output feedback control, robust filter design, and fault detection. A set of newly developed techniques such as piecewise analysis method, positively invariant set approach, event-triggered method, and cone complementary linearization approaches are presented. Control and Filtering for Semi-Markovian Jump Systems is a comprehensive reference for researcher and practitioners working in control engineering, system sciences and applied mathematics, and is also a useful source of information for senior undergraduates and graduates in these areas. The readers will benefit from some new concepts, new models and new methodologies with practical significance in control engineering and signal processing.

Автор: Candy
Название: Bayesian Signal Processing: Classical, Modern and Particle Filtering Methods
ISBN: 0470180943 ISBN-13(EAN): 9780470180945
Издательство: Wiley
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Цена: 119330.00 T
Наличие на складе: Поставка под заказ.
Описание: This book presents a unique viewpoint of signal processing from the Bayesian perspective in contrast to the pure statistical approach found in many textbooks. It features the next generation of processors that have recently been enabled with the advent of high speed/high throughput computers. The emphasis is on nonlinear/non-Gaussian problems, but classical techniques are included as special cases to enable the reader familiar with such methods to draw a parallel between the approaches. The common ground is the model sets. This text brings the reader from the classical methods of model-based signal processing including Kalman filtering for linear, linearized and approximate nonlinear processors as well as the recently developed unscented or sigma-point filters to the next generation of processors that will clearly dominate the future of model-based signal processing for years to come. Current applications (e.g. structures, tracking, equalization, biomedical) and simple examples to motivate the organization of the text are discussed. Examples are given to motivate all of the models and prepare the reader for further developments in subsequent chapters. In each case the processor along with accompanying simulations are discussed and applied to various data sets demonstrating the applicability and power of the Bayesian approach. The proposed text will be linked to the MATLAB (signal processing standard software) software package providing Notes as well as simple coding examples for illustrative purposes.