elms algorithm is the first two-channel adaptive filtering algorithm that takes into account the cross-correlation between the two input signals. The algorithm does not preprocess input signals, so it does not degrade...
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elms algorithm is the first two-channel adaptive filtering algorithm that takes into account the cross-correlation between the two input signals. The algorithm does not preprocess input signals, so it does not degrade the quality of the speech. However, a lot of computer simulation results show that elms algorithm has a bad performance. The elms algorithm is analyzed firstly, then a new algorithm is presented by modifying the block matrix used in elms algorithm to approximate input signals self-correlation matrix. The computer simulation results indicate that the improved algorithm has a better behavior than the elms algorithm.
In this paper, we proposed a new mixed variable step size elms algorithm(MVSS-elms) which combined the mean square error and the correlation of the error to modify the step size. The approach is general in the sense t...
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In this paper, we proposed a new mixed variable step size elms algorithm(MVSS-elms) which combined the mean square error and the correlation of the error to modify the step size. The approach is general in the sense that it not only retains the benefits of elms algorithm lower steady-state error, but also improves the system convergence speed. In comparison, the new approach performance is much better than VSS-elms algorithm and VFSSelms algorithm in the convergence speed and anti-noise capacity. Effectiveness of the proposed algorithm is demonstrated through computer simulations.
In this paper, we proposed a new mixed variable step size elms algorithm (MVSS-elms) which combined the mean square error and the correlation of the error to modify the step size. The approach is general in the sense ...
详细信息
In this paper, we proposed a new mixed variable step size elms algorithm (MVSS-elms) which combined the mean square error and the correlation of the error to modify the step size. The approach is general in the sense that it not only retains the benefits of elms algorithm lower steady-state error, but also improves the system convergence speed. In comparison, the new approach performance is much better than VSS-elms algorithm and VFSS-elms algorithm in the convergence speed and anti-noise capacity. Effectiveness of the proposed algorithm is demonstrated through computer simulations.
Following a brief discussion on basic LMS algorithms, the elms algorithms with steady small MSE is introduced. The new algorithm using the adaptive NLMS signal-estimator to predict the signal s(k). Forgetting factor ...
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Following a brief discussion on basic LMS algorithms, the elms algorithms with steady small MSE is introduced. The new algorithm using the adaptive NLMS signal-estimator to predict the signal s(k). Forgetting factor λi and error signal are used to control the step size update for iteration. Theoretic analysis and computer simulations demonstrate that the presented algorithm has good performance both in convergence properties and steady small misadjustment.
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