With independence assumption, this paper proposes and proves the superior step-size theorem on least mean square (LMS) algorithm, from the view of minimizing mean squared error (MSE). Following the theorem we construct a parallel variable step-size LMS filters algorithm. The theoretical model of the proposed algorithm is analyzed in detail. Simulations show the proposed theoretical model is quite close to the optimal variable step-size LMS (OVS-LMS) model. The experimental learning curves of the proposed algorithm also show the fastest convergence and fine tracking performance. The proposed algorithm is therefore a good realization of the OVS-LMS model.
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Gu, Y., Tang, K. & Cui, H. Superior step-size theorem and its application. Sci China Ser F 47, 151–160 (2004). https://doi.org/10.1360/02yf0108
- adaptive filtering
- superior step-size theorem
- parallel variable step-size LMS filters algorithm