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210123 ||| eng |
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|a 9781482253368
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|a 1482253364
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4 |
|a TK7872.F5
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|a Poularikas, Alexander D.
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|a Adaptive filtering
|b fundamentals of least mean squares with MATLAB
|c Alexander D. Poularikas
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| 260 |
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|a Boca Raton
|b CRC Press, Taylor & Francis
|c 2015
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| 300 |
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|a xviii, 343 pages
|b illustrations
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|a Includes bibliographical references and index
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|a Chapter 1. Vectors -- chapter 2. Matrices -- chapter 3. Processing of discrete deterministic signals : discrete systems -- chapter 4. Discrete-time random processes -- chapter 5. The Wiener filter -- chapter 6. Eigenvalues of Rx : properties of the error surface -- chapter 7. Newton's and steepest descent methods -- chapter 8. The least mean-square algorithm -- chapter 9. Variants of least mean-square algorithm
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| 653 |
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|a MATHEMATICS / Probability & Statistics / General / bisacsh
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| 653 |
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|a Filtres adaptatifs / Modèles mathématiques
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| 653 |
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|a Adaptive filters / Mathematical models
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| 653 |
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|a Adaptive signal processing / Mathematics
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| 653 |
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|a TECHNOLOGY & ENGINEERING / Mechanical / bisacsh
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| 653 |
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|a MATLAB / fast
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| 653 |
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|a Least squares / Data processing
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| 653 |
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|a MATLAB. / http://id.loc.gov/authorities/names/n92036881
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| 653 |
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|a Traitement adaptatif du signal / Mathématiques
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| 653 |
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|a Adaptive signal processing / Mathematics / fast
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| 653 |
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|a Adaptive filters / Mathematical models / fast
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| 653 |
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|a Least squares / Data processing / fast
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| 653 |
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|a TECHNOLOGY & ENGINEERING / Electrical / bisacsh
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|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 776 |
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|z 1482253356
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| 776 |
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|z 1482253364
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| 776 |
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|z 9781482253351
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| 776 |
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|z 9781482253368
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| 856 |
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|u https://learning.oreilly.com/library/view/~/9781482253351/?ar
|x Verlag
|3 Volltext
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|a 519.5
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|a 510
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|a 621.3822
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|a 620
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|a 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 distributionsExplains how to find the eigenvalues and eigenvectors of a
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