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Author : Simon Haykin,Bernard Widrow
Publisher : John Wiley & Sons
Page : 516 pages
File Size : 46,7 Mb
Release : 2003-09-08
Category : Technology & Engineering
ISBN : 0471215708
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.
Author : Alexander D. Poularikas
Publisher : CRC Press
Page : 261 pages
File Size : 40,5 Mb
Release : 2017-12-19
Category : Mathematics
ISBN : 9781351831024
Adaptive filters are used in many diverse applications, appearing in everything from military instruments to cellphones and home appliances. Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB® covers the core concepts of this important field, focusing on a vital part of the statistical signal processing area—the least mean square (LMS) adaptive filter. This largely self-contained text: Discusses random variables, stochastic processes, vectors, matrices, determinants, discrete random signals, and probability distributions Explains how to find the eigenvalues and eigenvectors of a matrix and the properties of the error surfaces Explores the Wiener filter and its practical uses, details the steepest descent method, and develops the Newton’s algorithm Addresses the basics of the LMS adaptive filter algorithm, considers LMS adaptive filter variants, and provides numerous examples Delivers a concise introduction to MATLAB®, supplying problems, computer experiments, and more than 110 functions and script files Featuring robust appendices complete with mathematical tables and formulas, Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB® clearly describes the key principles of adaptive filtering and effectively demonstrates how to apply them to solve real-world problems.
Author : Bei Xie,Tamal Bose
Publisher : Springer Nature
Page : 105 pages
File Size : 41,5 Mb
Release : 2022-05-31
Category : Technology & Engineering
ISBN : 9783031016813
Adaptive filters play an important role in the fields related to digital signal processing and communication, such as system identification, noise cancellation, channel equalization, and beamforming. In practical applications, the computational complexity of an adaptive filter is an important consideration. The Least Mean Square (LMS) algorithm is widely used because of its low computational complexity ($O(N)$) and simplicity in implementation. The least squares algorithms, such as Recursive Least Squares (RLS), Conjugate Gradient (CG), and Euclidean Direction Search (EDS), can converge faster and have lower steady-state mean square error (MSE) than LMS. However, their high computational complexity ($O(N^2)$) makes them unsuitable for many real-time applications. A well-known approach to controlling computational complexity is applying partial update (PU) method to adaptive filters. A partial update method can reduce the adaptive algorithm complexity by updating part of the weight vector instead of the entire vector or by updating part of the time. In the literature, there are only a few analyses of these partial update adaptive filter algorithms. Most analyses are based on partial update LMS and its variants. Only a few papers have addressed partial update RLS and Affine Projection (AP). Therefore, analyses for PU least-squares adaptive filter algorithms are necessary and meaningful. This monograph mostly focuses on the analyses of the partial update least-squares adaptive filter algorithms. Basic partial update methods are applied to adaptive filter algorithms including Least Squares CMA (LSCMA), EDS, and CG. The PU methods are also applied to CMA1-2 and NCMA to compare with the performance of the LSCMA. Mathematical derivation and performance analysis are provided including convergence condition, steady-state mean and mean-square performance for a time-invariant system. The steady-state mean and mean-square performance are also presented for a time-varying system. Computational complexity is calculated for each adaptive filter algorithm. Numerical examples are shown to compare the computational complexity of the PU adaptive filters with the full-update filters. Computer simulation examples, including system identification and channel equalization, are used to demonstrate the mathematical analysis and show the performance of PU adaptive filter algorithms. They also show the convergence performance of PU adaptive filters. The performance is compared between the original adaptive filter algorithms and different partial-update methods. The performance is also compared among similar PU least-squares adaptive filter algorithms, such as PU RLS, PU CG, and PU EDS. In addition to the generic applications of system identification and channel equalization, two special applications of using partial update adaptive filters are also presented. One application uses PU adaptive filters to detect Global System for Mobile Communication (GSM) signals in a local GSM system using the Open Base Transceiver Station (OpenBTS) and Asterisk Private Branch Exchange (PBX). The other application uses PU adaptive filters to do image compression in a system combining hyperspectral image compression and classification.
Author : Simon Haykin
Publisher : Unknown
Page : 128 pages
File Size : 44,6 Mb
Release : 2003-11-11
Category : Electronic
ISBN : 0471461334
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.
Author : Leonardo Rey Vega,Hernan Rey
Publisher : Springer Science & Business Media
Page : 122 pages
File Size : 48,5 Mb
Release : 2012-08-07
Category : Technology & Engineering
ISBN : 9783642302992
In this book, the authors provide insights into the basics of adaptive filtering, which are particularly useful for students taking their first steps into this field. They start by studying the problem of minimum mean-square-error filtering, i.e., Wiener filtering. Then, they analyze iterative methods for solving the optimization problem, e.g., the Method of Steepest Descent. By proposing stochastic approximations, several basic adaptive algorithms are derived, including Least Mean Squares (LMS), Normalized Least Mean Squares (NLMS) and Sign-error algorithms. The authors provide a general framework to study the stability and steady-state performance of these algorithms. The affine Projection Algorithm (APA) which provides faster convergence at the expense of computational complexity (although fast implementations can be used) is also presented. In addition, the Least Squares (LS) method and its recursive version (RLS), including fast implementations are discussed. The book closes with the discussion of several topics of interest in the adaptive filtering field.
Author : Wentao Ma,Badong Chen
Publisher : CRC Press
Page : 372 pages
File Size : 43,5 Mb
Release : 2024-05-31
Category : Computers
ISBN : 9781040015957
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.
Author : Simon S. Haykin
Publisher : Unknown
Page : 944 pages
File Size : 43,5 Mb
Release : 2002
Category : Adaptive filters
ISBN : UCSD:31822033473307
Adaptive Filter Theory, 4e, is ideal for courses in Adaptive Filters. Haykin examines both the mathematical theory behind various linear adaptive filters and the elements of supervised multilayer perceptrons. In its fourth edition, this highly successful book has been updated and refined to stay current with the field and develop concepts in as unified and accessible a manner as possible.
Author : Simon S. Haykin
Publisher : Unknown
Page : 240 pages
File Size : 44,8 Mb
Release : 1984
Category : Adaptive filters
ISBN : UOM:39015006421872
Author : Kevin Wagner,Milos Doroslovacki
Publisher : John Wiley & Sons
Page : 144 pages
File Size : 41,7 Mb
Release : 2013-07-01
Category : Computers
ISBN : 9781118579251
The topic of this book is proportionate-type normalized least mean squares (PtNLMS) adaptive filtering algorithms, which attempt to estimate an unknown impulse response by adaptively giving gains proportionate to an estimate of the impulse response and the current measured error. These algorithms offer low computational complexity and fast convergence times for sparse impulse responses in network and acoustic echo cancellation applications. New PtNLMS algorithms are developed by choosing gains that optimize user-defined criteria, such as mean square error, at all times. PtNLMS algorithms are extended from real-valued signals to complex-valued signals. The computational complexity of the presented algorithms is examined. Contents 1. Introduction to PtNLMS Algorithms 2. LMS Analysis Techniques 3. PtNLMS Analysis Techniques 4. Algorithms Designed Based on Minimization of User Defined Criteria 5. Probability Density of WD for PtLMS Algorithms 6. Adaptive Step-size PtNLMS Algorithms 7. Complex PtNLMS Algorithms 8. Computational Complexity for PtNLMS Algorithms About the Authors Kevin Wagner has been a physicist with the Radar Division of the Naval Research Laboratory, Washington, DC, USA since 2001. His research interests are in the area of adaptive signal processing and non-convex optimization. Milos Doroslovacki has been with the Department of Electrical and Computer Engineering at George Washington University, USA since 1995, where he is now an Associate Professor. His main research interests are in the fields of adaptive signal processing, communication signals and systems, discrete-time signal and system theory, and wavelets and their applications.
Author : Naresh R. Shanbhag,Keshab K. Parhi
Publisher : Springer Science & Business Media
Page : 194 pages
File Size : 54,9 Mb
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 9781461526780
Adaptive filtering is commonly used in many communication applications including speech and video predictive coding, mobile radio, ISDN subscriber loops, and multimedia systems. Existing adaptive filtering topologies are non-concurrent and cannot be pipelined. Pipelined Adaptive Digital Filters presents new pipelined topologies which are useful in reducing area and power and in increasing speed. If the adaptive filter portion of a system suffers from a power-speed-area bottleneck, a solution is provided. Pipelined Adaptive Digital Filters is required reading for all users of adaptive digital filtering algorithms. Algorithm, application and integrated circuit chip designers can learn how their algorithms can be tailored and implemented with lower area and power consumption and with higher speed. The relaxed look-ahead techniques are used to design families of new topologies for many adaptive filtering applications including least mean square and lattice adaptive filters, adaptive differential pulse code modulation coders, adaptive differential vector quantizers, adaptive decision feedback equalizers and adaptive Kalman filters. Those who use adaptive filtering in communications, signal and image processing algorithms can learn the basis of relaxed look-ahead pipelining and can use their own relaxations to design pipelined topologies suitable for their applications. Pipelined Adaptive Digital Filters is especially useful to designers of communications, speech, and video applications who deal with adaptive filtering, those involved with design of modems, wireless systems, subscriber loops, beam formers, and system identification applications. This book can also be used as a text for advanced courses on the topic.
Author : Sabu M. Thampi,Alexander Gelbukh,Jayanta Mukhopadhyay
Publisher : Springer Science & Business Media
Page : 612 pages
File Size : 53,7 Mb
Release : 2014-02-14
Category : Technology & Engineering
ISBN : 9783319049601
This edited volume contains a selection of refereed and revised papers originally presented at the International Symposium on Signal Processing and Intelligent Recognition Systems (SIRS-2014), March 13-15, 2014, Trivandrum, India. The program committee received 134 submissions from 11 countries. Each paper was peer reviewed by at least three or more independent referees of the program committee and the 52 papers were finally selected. The papers offer stimulating insights into Pattern Recognition, Machine Learning and Knowledge-Based Systems; Signal and Speech Processing; Image and Video Processing; Mobile Computing and Applications and Computer Vision. The book is directed to the researchers and scientists engaged in various field of signal processing and related areas.
Author : Alexander D. Poularikas,Zayed M. Ramadan
Publisher : CRC Press
Page : 240 pages
File Size : 41,5 Mb
Release : 2017-12-19
Category : Technology & Engineering
ISBN : 9781420006384
Because of the wide use of adaptive filtering in digital signal processing and, because most of the modern electronic devices include some type of an adaptive filter, a text that brings forth the fundamentals of this field was necessary. The material and the principles presented in this book are easily accessible to engineers, scientists, and students who would like to learn the fundamentals of this field and have a background at the bachelor level. Adaptive Filtering Primer with MATLAB® clearly explains the fundamentals of adaptive filtering supported by numerous examples and computer simulations. The authors introduce discrete-time signal processing, random variables and stochastic processes, the Wiener filter, properties of the error surface, the steepest descent method, and the least mean square (LMS) algorithm. They also supply many MATLAB® functions and m-files along with computer experiments to illustrate how to apply the concepts to real-world problems. The book includes problems along with hints, suggestions, and solutions for solving them. An appendix on matrix computations completes the self-contained coverage. With applications across a wide range of areas, including radar, communications, control, medical instrumentation, and seismology, Adaptive Filtering Primer with MATLAB® is an ideal companion for quick reference and a perfect, concise introduction to the field.
Author : Weifeng Liu,José C. Principe,Simon Haykin
Publisher : Wiley
Page : 240 pages
File Size : 52,5 Mb
Release : 2010-03-01
Category : Science
ISBN : 0470447532
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 : Danilo P. Mandic,Vanessa Su Lee Goh
Publisher : John Wiley & Sons
Page : 344 pages
File Size : 54,9 Mb
Release : 2009-04-20
Category : Science
ISBN : 9780470742631
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 : Odile Macchi
Publisher : Wiley
Page : 476 pages
File Size : 49,5 Mb
Release : 1995-05-09
Category : Technology & Engineering
ISBN : 0471934038
Adaptive Processing The Least Mean Squares Approach with Applications in Transmission Odile Macchi Laboratoire des Signaux et Systèmes France Providing an in-depth study of adaptive systems used in digital signal processing, this book presents both theoretical concepts and applications. The author provides a rigorous investigation of LMS adaptive processing and exemplifies the concepts with channel data equalisation, echo cancellation and prediction for bit rate reduction. The text is divided into four key areas: Adaptive transversal filters, covering their transient aspects (speed of convergence) and their steady-state (fluctuations and misadjustment). Implementation aspects (binary word lengths and simplified sign algorithms). Tracking performance of adaptive filters in a time varying context. Adaptive recursive filters and their stability problems. This book presents a comprehensive mathematical treatment of adaptive processes based on realistic assumptions such as the finite memory of inputs. The author uses original research material organised in a unified framework. Particularly original are the chapters on sign algorithms, tracking performance and recursive filters in the presence of narrowband inputs. This comprehensive text will be of considerable interest to research students in digital communications and signal processing. In particular, this will be a valuable reference for professional practitioners working in the industrial R & D market.