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Proceedings Paper

Improved adaptive convex combination of LMS algorithm based on conjugate gradient method
Author(s): Leya Zeng; Hua Xu; Tianrui Wang
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Paper Abstract

The convergence speed of a single least mean square (LMS) filter contradicts its stable state error incompatibly. Such a situation significantly restrains the performance of the recognition system. The convex combination of least mean square (CLMS) algorithm is employed in this paper to ensure that had good output. However, the rule for modifying mixing parameter is based on the steepest descent method, when the algorithm converges, it will take a lot of detours and do a lot of hard. In order to settle this problem, a new rule based on the conjugate gradient method is proposed in this paper. Meanwhile, modified hyperbolic tangent function is used to reduce computational complexity. Theoretical analysis and simulation results demonstrate that under different simulation environment, the proposed algorithm performs good property of mean square and tracking.

Paper Details

Date Published: 29 August 2016
PDF: 6 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 1003351 (29 August 2016); doi: 10.1117/12.2244843
Show Author Affiliations
Leya Zeng, Air Force Engineering Univ. (China)
Hua Xu, Air Force Engineering Univ. (China)
Tianrui Wang, Nanjing Normal Univ. (China)


Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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