Kernel Adaptive Filtering
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Kernel Adaptive Filtering
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Author : Weifeng Liu
language : en
Publisher: John Wiley & Sons
Release Date : 2011-09-20
Kernel Adaptive Filtering written by Weifeng Liu and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-20 with Science categories.
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.
Adaptive Filtering Under Minimum Mean P Power Error Criterion
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Author : Wentao Ma
language : en
Publisher: CRC Press
Release Date : 2024-05-31
Adaptive Filtering Under Minimum Mean P Power Error Criterion written by Wentao Ma and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-31 with Computers categories.
Adaptive filtering still receives attention in engineering as the use of the adaptive filter provides improved performance over the use of a fixed filter under the time-varying and unknown statistics environments. This application evolved communications, signal processing, seismology, mechanical design, and control engineering. The most popular optimization criterion in adaptive filtering is the well-known minimum mean square error (MMSE) criterion, which is, however, only optimal when the signals involved are Gaussian-distributed. Therefore, many "optimal solutions" under MMSE are not optimal. As an extension of the traditional MMSE, the minimum mean p-power error (MMPE) criterion has shown superior performance in many applications of adaptive filtering. This book aims to provide a comprehensive introduction of the MMPE and related adaptive filtering algorithms, which will become an important reference for researchers and practitioners in this application area. The book is geared to senior undergraduates with a basic understanding of linear algebra and statistics, graduate students, or practitioners with experience in adaptive signal processing. Key Features: Provides a systematic description of the MMPE criterion. Many adaptive filtering algorithms under MMPE, including linear and nonlinear filters, will be introduced. Extensive illustrative examples are included to demonstrate the results.
Efficient Nonlinear Adaptive Filters
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Author : Haiquan Zhao
language : en
Publisher: Springer Nature
Release Date : 2023-02-10
Efficient Nonlinear Adaptive Filters written by Haiquan Zhao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-10 with Technology & Engineering categories.
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.
From Fixed To Adaptive Budget Robust Kernel Adaptive Filtering
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Author : Songlin Zhao
language : en
Publisher:
Release Date : 2012
From Fixed To Adaptive Budget Robust Kernel Adaptive Filtering written by Songlin Zhao and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
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.
Least Mean Square Adaptive Filters
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Author : Simon Haykin
language : en
Publisher: John Wiley & Sons
Release Date : 2003-09-08
Least Mean Square Adaptive Filters written by Simon Haykin and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-09-08 with Technology & Engineering categories.
Edited by the original inventor of the technology. Includes contributions by the foremost experts in the field. The only book to cover these topics together.
Online Learning And Adaptive Filters
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Author : Paulo S. R. Diniz
language : en
Publisher: Cambridge University Press
Release Date : 2022-12-08
Online Learning And Adaptive Filters written by Paulo S. R. Diniz and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-08 with Computers categories.
Discover up-to-date techniques and algorithms in this concise, intuitive text, with extensive solutions for challenging learning problems.
Complex Valued Nonlinear Adaptive Filters
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Author : Danilo P. Mandic
language : en
Publisher: John Wiley & Sons
Release Date : 2009-04-20
Complex Valued Nonlinear Adaptive Filters written by Danilo P. Mandic and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-20 with Science categories.
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.
Adaptive Learning Methods For Nonlinear System Modeling
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Author : Danilo Comminiello
language : en
Publisher: Butterworth-Heinemann
Release Date : 2018-07-05
Adaptive Learning Methods For Nonlinear System Modeling written by Danilo Comminiello and has been published by Butterworth-Heinemann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-05 with Technology & Engineering categories.
Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identification. Real-life problems always entail a certain degree of nonlinearity, which makes linear models a non-optimal choice. This book mainly focuses on those methodologies for nonlinear modeling that involve any adaptive learning approaches to process data coming from an unknown nonlinear system. By learning from available data, such methods aim at estimating the nonlinearity introduced by the unknown system. In particular, the methods presented in this book are based on online learning approaches, which process the data example-by-example and allow to model even complex nonlinearities, e.g., showing time-varying and dynamic behaviors. Possible fields of applications of such algorithms includes distributed sensor networks, wireless communications, channel identification, predictive maintenance, wind prediction, network security, vehicular networks, active noise control, information forensics and security, tracking control in mobile robots, power systems, and nonlinear modeling in big data, among many others. This book serves as a crucial resource for researchers, PhD and post-graduate students working in the areas of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, computational intelligence. This book may be also of interest to the industry market and practitioners working with a wide variety of nonlinear systems. Presents the key trends and future perspectives in the field of nonlinear signal processing and adaptive learning. Introduces novel solutions and improvements over the state-of-the-art methods in the very exciting area of online and adaptive nonlinear identification. Helps readers understand important methods that are effective in nonlinear system modelling, suggesting the right methodology to address particular issues.
Information Theoretic Learning
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Author : Jose C. Principe
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-04-06
Information Theoretic Learning written by Jose C. Principe and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-04-06 with Computers categories.
This book is the first cohesive treatment of ITL algorithms to adapt linear or nonlinear learning machines both in supervised and unsupervised paradigms. It compares the performance of ITL algorithms with the second order counterparts in many applications.
Digital Signal Processing With Kernel Methods
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Author : Jose Luis Rojo-Alvarez
language : en
Publisher: John Wiley & Sons
Release Date : 2018-02-05
Digital Signal Processing With Kernel Methods written by Jose Luis Rojo-Alvarez and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-05 with Technology & Engineering categories.
A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors: http://github.com/DSPKM • Presents the necessary basic ideas from both digital signal processing and machine learning concepts • Reviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing • Surveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing An excellent book for signal processing researchers and practitioners, Digital Signal Processing with Kernel Methods will also appeal to those involved in machine learning and pattern recognition.