Adaptive Filtering Prediction and Control PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Adaptive Filtering Prediction and Control PDF full book. Access full book title Adaptive Filtering Prediction and Control by Graham C Goodwin. Download full books in PDF and EPUB format.
Author: Graham C Goodwin
Publisher: Courier Corporation
ISBN: 0486137724
Category : Technology & Engineering
Languages : en
Pages : 562
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Book Description
This unified survey focuses on linear discrete-time systems and explores natural extensions to nonlinear systems. It emphasizes discrete-time systems, summarizing theoretical and practical aspects of a large class of adaptive algorithms. 1984 edition.
Author: Graham C Goodwin
Publisher: Courier Corporation
ISBN: 0486137724
Category : Technology & Engineering
Languages : en
Pages : 562
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Book Description
This unified survey focuses on linear discrete-time systems and explores natural extensions to nonlinear systems. It emphasizes discrete-time systems, summarizing theoretical and practical aspects of a large class of adaptive algorithms. 1984 edition.
Author: Shankar Sastry
Publisher: Courier Corporation
ISBN: 0486482022
Category : Technology & Engineering
Languages : en
Pages : 402
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Book Description
This volume surveys the major results and techniques of analysis in the field of adaptive control. Focusing on linear, continuous time, single-input, single-output systems, the authors offer a clear, conceptual presentation of adaptive methods, enabling a critical evaluation of these techniques and suggesting avenues of further development. 1989 edition.
Author: K.J. Aström
Publisher: Springer Science & Business Media
ISBN: 1441985689
Category : Science
Languages : en
Pages : 404
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Book Description
The area of adaptive systems, which encompasses recursive identification, adaptive control, filtering, and signal processing, has been one of the most active areas of the past decade. Since adaptive controllers are fundamentally nonlinear controllers which are applied to nominally linear, possibly stochastic and time-varying systems, their theoretical analysis is usually very difficult. Nevertheless, over the past decade much fundamental progress has been made on some key questions concerning their stability, convergence, performance, and robustness. Moreover, adaptive controllers have been successfully employed in numerous practical applications, and have even entered the marketplace.
Author: Danilo P. Mandic
Publisher: John Wiley & Sons
ISBN: 0470742631
Category : Science
Languages : en
Pages : 344
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Book Description
This book was written in response to the growing demand for a text that provides a unified treatment of linear and nonlinear complex valued adaptive filters, and methods for the processing of general complex signals (circular and noncircular). It brings together adaptive filtering algorithms for feedforward (transversal) and feedback architectures and the recent developments in the statistics of complex variable, under the powerful frameworks of CR (Wirtinger) calculus and augmented complex statistics. This offers a number of theoretical performance gains, which is illustrated on both stochastic gradient algorithms, such as the augmented complex least mean square (ACLMS), and those based on Kalman filters. This work is supported by a number of simulations using synthetic and real world data, including the noncircular and intermittent radar and wind signals.
Author: Weifeng Liu
Publisher: Wiley
ISBN: 9780470447536
Category : Science
Languages : en
Pages : 240
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Book Description
Online learning from a signal processing perspective There is increased interest in kernel learning algorithms inneural networks and a growing need for nonlinear adaptivealgorithms in advanced signal processing, communications, andcontrols. Kernel Adaptive Filtering is the first book topresent a comprehensive, unifying introduction to online learningalgorithms in reproducing kernel Hilbert spaces. Based on researchbeing conducted in the Computational Neuro-Engineering Laboratoryat the University of Florida and in the Cognitive SystemsLaboratory at McMaster University, Ontario, Canada, this uniqueresource elevates the adaptive filtering theory to a new level,presenting a new design methodology of nonlinear adaptivefilters. Covers the kernel least mean squares algorithm, kernel affineprojection algorithms, the kernel recursive least squaresalgorithm, the theory of Gaussian process regression, and theextended kernel recursive least squares algorithm Presents a powerful model-selection method called maximummarginal likelihood Addresses the principal bottleneck of kernel adaptivefilters—their growing structure Features twelve computer-oriented experiments to reinforce theconcepts, with MATLAB codes downloadable from the authors' Website Concludes each chapter with a summary of the state of the artand potential future directions for original research Kernel Adaptive Filtering is ideal for engineers,computer scientists, and graduate students interested in nonlinearadaptive systems for online applications (applications where thedata stream arrives one sample at a time and incremental optimalsolutions are desirable). It is also a useful guide for those wholook for nonlinear adaptive filtering methodologies to solvepractical problems.
Author: P. R. Kumar
Publisher: SIAM
ISBN: 1611974259
Category : Mathematics
Languages : en
Pages : 371
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Book Description
Since its origins in the 1940s, the subject of decision making under uncertainty has grown into a diversified area with application in several branches of engineering and in those areas of the social sciences concerned with policy analysis and prescription. These approaches required a computing capacity too expensive for the time, until the ability to collect and process huge quantities of data engendered an explosion of work in the area. This book provides succinct and rigorous treatment of the foundations of stochastic control; a unified approach to filtering, estimation, prediction, and stochastic and adaptive control; and the conceptual framework necessary to understand current trends in stochastic control, data mining, machine learning, and robotics.
Author: Weifeng Liu
Publisher: John Wiley & Sons
ISBN: 1118211219
Category : Science
Languages : en
Pages : 167
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Book Description
Online learning from a signal processing perspective There is increased interest in kernel learning algorithms in neural networks and a growing need for nonlinear adaptive algorithms in advanced signal processing, communications, and controls. Kernel Adaptive Filtering is the first book to present a comprehensive, unifying introduction to online learning algorithms in reproducing kernel Hilbert spaces. Based on research being conducted in the Computational Neuro-Engineering Laboratory at the University of Florida and in the Cognitive Systems Laboratory at McMaster University, Ontario, Canada, this unique resource elevates the adaptive filtering theory to a new level, presenting a new design methodology of nonlinear adaptive filters. Covers the kernel least mean squares algorithm, kernel affine projection algorithms, the kernel recursive least squares algorithm, the theory of Gaussian process regression, and the extended kernel recursive least squares algorithm Presents a powerful model-selection method called maximum marginal likelihood Addresses the principal bottleneck of kernel adaptive filters—their growing structure Features twelve computer-oriented experiments to reinforce the concepts, with MATLAB codes downloadable from the authors' Web site Concludes each chapter with a summary of the state of the art and potential future directions for original research Kernel Adaptive Filtering is ideal for engineers, computer scientists, and graduate students interested in nonlinear adaptive systems for online applications (applications where the data stream arrives one sample at a time and incremental optimal solutions are desirable). It is also a useful guide for those who look for nonlinear adaptive filtering methodologies to solve practical problems.
Author: Wei Ren
Publisher:
ISBN:
Category : Control theory
Languages : en
Pages : 134
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Book Description
This thesis examines the basic asymptotic properties of various stochastic adaptive systems for identification, filtering, prediction and control. These include the convergence of long-term averages of signals of interest (self-optimality), the convergence of adaptive filters or controllers (self-tuning property), the convergence of parameter estimates, and the rates of convergence. This thesis divides itself naturally into two parts. The first part considers identification, adaptive prediction and control based on the ARMAX model, while the second part considers general stochastic parallel model adaptation problems, which include output error identification, adaptive IIR filtering, adaptive noise cancelling, and adaptive feedforward control with or without input contamination. In the first part, the use of a generalized certainty equivalence approach in which the estimates of disturbance as well as parameters are utilized is proposed. Based on this, the self-optimality of adaptive minimum variance prediction and model reference adaptive control is established for systems with general delay and colored noise. Both direct and indirect approaches based on the extended least squares as well as the stochastic gradient algorithms are considered. For the direct approach, it is shown that interlacing is not necessary for convergence, thus resolving this long-standing open problem. Concerning the self-tuning property, it is established that self-optimality in the mean square sense, in general, implies self-tuning, by exhibiting the convergence of the parameter estimates to the null space of a certain covariance matrix, and by characterizing this null space. It is found that adaptive minimum variance regulators self-tune because of the "internal excitation" due to the plant disturbance alone. Finally, the exact order of external excitation required for the parameter estimates to converge to the true parameter is determined. In the second part of the thesis, the convergence of several parallel model adaptation schemes in the presence of nonstationary colored noise is established. A special case of our results resolves the long-standing problem of the convergence and unbiasedness of the output error identification scheme in the presence of colored noise. We also develop a simple general technique for analyzing the strong consistency of parameter estimation with projection. Of pedagogical interest is the deterministic reduction viewpoint we adopt in which all relevant properties of stochastically modeled disturbances are characterized deterministically by some long-term average properties. Readers more familiar with deterministic theory may well find this viewpoint to be more enlightening with respect to understanding the goals and results of stochastic adaptive system theory.
Author: Behrouz Farhang-Boroujeny
Publisher: John Wiley & Sons
ISBN: 111859133X
Category : Technology & Engineering
Languages : en
Pages : 800
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Book Description
This second edition of Adaptive Filters: Theory and Applications has been updated throughout to reflect the latest developments in this field; notably an increased coverage given to the practical applications of the theory to illustrate the much broader range of adaptive filters applications developed in recent years. The book offers an easy to understand approach to the theory and application of adaptive filters by clearly illustrating how the theory explained in the early chapters of the book is modified for the various applications discussed in detail in later chapters. This integrated approach makes the book a valuable resource for graduate students; and the inclusion of more advanced applications including antenna arrays and wireless communications makes it a suitable technical reference for engineers, practitioners and researchers. Key features: • Offers a thorough treatment of the theory of adaptive signal processing; incorporating new material on transform domain, frequency domain, subband adaptive filters, acoustic echo cancellation and active noise control. • Provides an in-depth study of applications which now includes extensive coverage of OFDM, MIMO and smart antennas. • Contains exercises and computer simulation problems at the end of each chapter. • Includes a new companion website hosting MATLAB® simulation programs which complement the theoretical analyses, enabling the reader to gain an in-depth understanding of the behaviours and properties of the various adaptive algorithms.
Author: Paulo Sergio Ramirez Diniz
Publisher: Springer Science & Business Media
ISBN: 9781402071256
Category : Adaptive filters
Languages : en
Pages : 594
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Book Description
Adaptive Filtering: Algorithms and Practical Implementation, Second Edition, presents a concise overview of adaptive filtering, covering as many algorithms as possible in a unified form that avoids repetition and simplifies notation. It is suitable as a textbook for senior undergraduate or first-year graduate courses in adaptive signal processing and adaptive filters. The philosophy of the presentation is to expose the material with a solid theoretical foundation, to concentrate on algorithms that really work in a finite-precision implementation, and to provide easy access to working algorithms. Hence, practicing engineers and scientists will also find the book to be an excellent reference. This second edition contains a substantial amount of new material: -Two new chapters on nonlinear and subband adaptive filtering; -Linearly constrained Weiner filters and LMS algorithms; -LMS algorithm behavior in fast adaptation; -Affine projection algorithms; -Derivation smoothing; -MATLAB codes for algorithms. An instructor's manual, a set of master transparencies, and the MATLAB codes for all of the algorithms described in the text are also available. Useful to both professional researchers and students, the text includes 185 problems; over 38 examples, and over 130 illustrations. It is of primary interest to those working in signal processing, communications, and circuits and systems. It will also be of interest to those working in power systems, networks, learning systems, and intelligent systems.
