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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: Weifeng Liu
Publisher: John Wiley & Sons
ISBN: 1118211219
Category : Science
Languages : en
Pages : 167
Get Book Here
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: Paulo S.R. Diniz
Publisher: Springer Science & Business Media
ISBN: 1475736371
Category : Technology & Engineering
Languages : en
Pages : 582
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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.
Author: John R. Treichler
Publisher: Wiley-Interscience
ISBN:
Category : Mathematics
Languages : en
Pages : 376
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Book Description
A comprehensive compilation of adaptive filtering concepts, algorithm forms, behavioral insights, and application guidelines useful for the engineer interested in designing appropriate adaptive filters for various applications and for students needing a cohesive pedagogy for initiation of basic research in adaptive theory. The analysis and design of three basic classes of adaptive filters are presented: adaptive finite-impulse-response (FIR) filters; adaptive infinite-impulse-response (IRR) filters; and adaptive property restoring filters. For the widely used FIR filters, the book offers the most popular analytical tools and distills a tutorial collection of insightful design guidelines of proven utility. For the more recently developed filters, it focuses on emerging theoretical foundations and suggested applications. The material is supplemented with listings of FORTRAN codes for basic algorithms and a real-time solution to one adaptive FIR filter problem using a Texas Instruments signal processing chip.
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.
Author: Haiquan Zhao
Publisher: Springer Nature
ISBN: 3031208188
Category : Technology & Engineering
Languages : en
Pages : 271
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Book Description
This book presents the design, analysis, and application of nonlinear adaptive filters with the goal of improving efficient performance (ie the convergence speed, steady-state error, and computational complexity). The authors present a nonlinear adaptive filter, which is an important part of nonlinear system and digital signal processing and can be applied to diverse fields such as communications, control power system, radar sonar, etc. The authors also present an efficient nonlinear filter model and robust adaptive filtering algorithm based on the local cost function of optimal criterion to overcome non-Gaussian noise interference. The authors show how these achievements provide new theories and methods for robust adaptive filtering of nonlinear and non-Gaussian systems. The book is written for the scientist and engineer who are not necessarily an expert in the specific nonlinear filtering field but who want to learn about the current research and application. The book is also written to accompany a graduate/PhD course in the area of nonlinear system and adaptive signal processing.
Author: Wenping Cao
Publisher: BoD – Books on Demand
ISBN: 1839623772
Category : Computers
Languages : en
Pages : 154
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Book Description
Active filters are key technologies in applications such as telecommunications, advanced control, smart grids, and green transport. This book provides an update of the latest technological progress in signal processing and adaptive filters, with a focus on Kalman filters and applications. It illustrates fundamentals and guides filter design for specific applications, primarily for graduate students, academics, and industrial engineers who are interested in the theoretical, experimental, and design aspects of active filter technologies.
Author: Fouad Sabry
Publisher: One Billion Knowledgeable
ISBN:
Category : Computers
Languages : en
Pages : 130
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Book Description
What is Adaptive Filter A system that has a linear filter and possesses a transfer function that is controlled by variable parameters as well as a means to alter those parameters in accordance with an optimization technique is commonly referred to as an adaptive filter. The vast majority of adaptive filters are digital filters. This is due to the complexity of the optimization techniques. Some applications necessitate the utilization of adaptive filters due to the fact that some parameters of the desired processing operation are either unknown in advance or are frequently subject to change. Refining the transfer function of the closed loop adaptive filter is accomplished by the utilization of feedback in the form of an error signal. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Adaptive filter Chapter 2: Signal-to-noise ratio Chapter 3: Additive white Gaussian noise Chapter 4: Linear elasticity Chapter 5: Sliding mode control Chapter 6: Array processing Chapter 7: Autoregressive model Chapter 8: Least mean squares filter Chapter 9: Recursive least squares filter Chapter 10: ADALINE (II) Answering the public top questions about adaptive filter. (III) Real world examples for the usage of adaptive filter in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Adaptive Filter.
Author: Lino Garcia Morales
Publisher: BoD – Books on Demand
ISBN: 9535109987
Category : Computers
Languages : en
Pages : 165
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Book Description
Adaptive filtering can be used to characterize unknown systems in time-variant environments. The main objective of this approach is to meet a difficult comprise: maximum convergence speed with maximum accuracy. Each application requires a certain approach which determines the filter structure, the cost function to minimize the estimation error, the adaptive algorithm, and other parameters; and each selection involves certain cost in computational terms, that in any case should consume less time than the time required by the application working in real-time. Theory and application are not, therefore, isolated entities but an imbricated whole that requires a holistic vision. This book collects some theoretical approaches and practical applications in different areas that support expanding of adaptive systems.
Author: Ali H. Sayed
Publisher:
ISBN: 9788126528776
Category :
Languages : en
Pages : 1168
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Book Description
Special Features: Designed to the most comprehensive book on the market today providing instructors a wide choice in designing their courses." Offer computer problems to illustrate real life applications for students and professionals alike." Written by an award-winning author" Based on a graduate level course offered by the author at UCLA and has class tested there and at other universities over a number of years" There will be an Instructor's Manual presenting detailed solutions to all problems in the book." Each chapter in the book consists of five distinctive parts in the following order: concepts, notes and bibliography, problems, appendixes and computer projects. About The Book: This book is intended for a graduate course on adaptive filtering and is based on the author's course offered at UCLA over a number of years. Each chapter in the book consists of five distinctive parts in the following order: concepts, notes and bibliography, problems, appendixes and computer projects. The computer projects have been chosen to be relevant for practitioners and to show students how the theory can be applied to real situations.
Author: Songlin Zhao
Publisher:
ISBN:
Category :
Languages : en
Pages : 122
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Book Description
Indeed the issue is how to deal with the trade-off between system complexity and accuracy performance, and an information learning criterion called Minimal Description Length (MDL) is introduced to kernel adaptive filtering. Two formulations of MDL: batch and online model are developed and illustrated by approximation level selection in KRLS-ALD and center dictionary selection in KLMS respectively. The end result is a methodology that controls the kernel adaptive filter dictionary (model order) according to the complexity of the true system and the input signal for online learning even in nonstationary environments.
