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Partial update least-square adaptive filtering /
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 consid...
| Egile Nagusiak: | , |
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| Formatua: | eBook |
| Hizkuntza: | English |
| Argitaratua: |
San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) :
Morgan & Claypool,
2014.
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| Saila: | Synthesis digital library of engineering and computer science.
Synthesis lectures on communications ; # 10. |
| Gaiak: | |
| Sarrera elektronikoa: | Abstract with links to full text |
| Gaia: | 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(N2)) 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. |
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| Alearen deskribapena: | Part of: Synthesis digital library of engineering and computer science. Series from website. |
| Deskribapen fisikoa: | 1 PDF (ix, 105 pages) : illustrations. Also available in print. |
| Formatua: | Mode of access: World Wide Web. System requirements: Adobe Acrobat Reader. |
| Bibliografia: | Includes bibliographical references (pages 99-103). |
| ISBN: | 9781627052320 |
| ISSN: | 1932-1708 ; |
| Sartu: | Abstract freely available; full-text restricted to subscribers or individual document purchasers. |