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Titlebook: Adaptive Signal Processing; Theory and Applicati S. Thomas Alexander Book 1986 Springer-Verlag New York Inc. 1986 Signal.bridge.communicati

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发表于 2025-3-21 19:01:16 | 显示全部楼层 |阅读模式
期刊全称Adaptive Signal Processing
期刊简称Theory and Applicati
影响因子2023S. Thomas Alexander
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学科分类Monographs in Computer Science
图书封面Titlebook: Adaptive Signal Processing; Theory and Applicati S. Thomas Alexander Book 1986 Springer-Verlag New York Inc. 1986 Signal.bridge.communicati
影响因子The creation of the text really began in 1976 with the author being involved with a group of researchers at Stanford University and the Naval Ocean Systems Center, San Diego. At that time, adaptive techniques were more laboratory (and mental) curiosities than the accepted and pervasive categories of signal processing that they have become. Over the lasl 10 years, adaptive filters have become standard components in telephony, data communications, and signal detection and tracking systems. Their use and consumer acceptance will undoubtedly only increase in the future. The mathematical principles underlying adaptive signal processing were initially fascinating and were my first experience in seeing applied mathematics work for a paycheck. Since that time, the application of even more advanced mathematical techniques have kept the area of adaptive signal processing as exciting as those initial days. The text seeks to be a bridge between the open literature in the professional journals, which is usually quite concentrated, concise, and advanced, and the graduate classroom and research environment where underlying principles are often more important.
Pindex Book 1986
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Kerstin Hoffmann-Wagner,Gudrun Jostesno specific method was presented for solving the normal equation (2.3.5).for the desired linear prediction filter ... The only topic of interest then was the nonsingularity of the autocorrelation matrix ... If .. is nonsingular, then the inverse R.. is nonsingular, then the inverse R. exists, and the theoretical solution is given by
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Barrierefrei kommunizieren für Unternehmenme varying or unknown or possibly both. These types of processes represent some of the most difficult problems in transmitting and receiving information. The area of adaptive signal processing techniques provides one approach for removing distortion in communications, as well as extracting informati
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Kerstin Hoffmann-Wagner,Gudrun Jostesno specific method was presented for solving the normal equation (2.3.5).for the desired linear prediction filter ... The only topic of interest then was the nonsingularity of the autocorrelation matrix ... If .. is nonsingular, then the inverse R.. is nonsingular, then the inverse R. exists, and th
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Definition der zentralen Begriffe, 5, the LMS adaptive filter algorithm was seen to be a natural candidate for implementing the transversal form for an adaptive linear prediction filter. However, in Chapter 3, an alternative structure denoted as the lattice formulation was seen to be a direct result of using Durbin’s method for solv
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Interpretation der Studienergebnisse,5). Implicit in this approach is that the . error measure of mean square prediction error is being minimized. The least mean squares (LMS) algorithm of Chapter 5 emulated the approach of the method of steepest descent for solving the normal equations, whereas the gradient lattice method of Chapter 7
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Werkzeuge zur Identifikation von Barrieren,very powerful mathematics of linear vector spaces. Through the use of vector space concepts, many intuitive geometrical interpretations result that will enable the reader to understand many approaches currently used in the literature. Both the least squares lattice (LSL) and the least squares transv
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