Improved adaptive convex combination of LMS algorithm based on conjugate gradient method

Leya Zeng; Hua Xu; Tianrui Wang

doi:10.1117/12.2244843

29 August 2016 Improved adaptive convex combination of LMS algorithm based on conjugate gradient method

Leya Zeng, Hua Xu, Tianrui Wang

Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 1003351 (2016) https://doi.org/10.1117/12.2244843
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China

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.

Citation Download Citation

Leya Zeng, Hua Xu, and Tianrui Wang "Improved adaptive convex combination of LMS algorithm based on conjugate gradient method", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 1003351 (29 August 2016); https://doi.org/10.1117/12.2244843

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