| 書目名稱 | Partial Update Least-Square Adaptive Filtering | | 編輯 | Bei Xie,Tamal Bose | | 視頻video | http://file.papertrans.cn/742/741560/741560.mp4 | | 叢書名稱 | Synthesis Lectures on Communications | | 圖書封面 |  | | 描述 | 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 | | 出版日期 | Book 2014 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-01681-3 | | isbn_softcover | 978-3-031-00553-4 | | isbn_ebook | 978-3-031-01681-3Series ISSN 1932-1244 Series E-ISSN 1932-1708 | | issn_series | 1932-1244 | | copyright | Springer Nature Switzerland AG 2014 |
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