By L. Morales
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Extra info for Adaptive Filtering
4. Simulation results In section Ⅲ, some well-known real and complex adaptive algorithms, such as LMS algorithm, LMF algorithm and LMMN algorithm have shown the accuracy of the 34 Adaptive Filtering corresponding analysis results. 5 ), which have not been involved in the previous literatures. 1 Simulation model In all the cases, a 11-tap adaptive filter with tap-centered initialization is used. The data are generated according to model (3), the experimental value for different step-size is obtained by running adaptive algorithm for different iteration number and averaging the squareserror curve over 60 experiments in order to generate the ensemble-average curve.
2. 2. Steady-State Performance Analyses of Adaptive Filters Fig. 3. 1. Fig. 4. 2. 37 38 Adaptive Filtering Fig. 5. 1. Fig. 6. 1 Steady-State Performance Analyses of Adaptive Filters Fig. 7. 1. Fig. 8. 1. 39 40 Adaptive Filtering Fig. 9. 2. Fig. 10. 1. Steady-State Performance Analyses of Adaptive Filters Fig. 11. 2. Fig. 12. Comparisons of the tracking performance between LMS algorithm and LMP algorithm in Gaussian noise environments and uniformly distributed noise environments. 41 42 Adaptive Filtering 5.
These expressions are all second-order approximate. For some well-known adaptive algorithms, such as least-mean-square (LMS) algorithm, least-mean-forth (LMF) algorithm and least-mean-mixed norm (LMMN) algorithm, the proposed results are all the same as those summarized by A. H. Sayed in . , -NLMMN and -NLMP), their steady-state performances are also investigated. In addition, comparisons with tracking ability between LMP algorithm with p 2 and LMS algorithm, show that the superiority of the LMS algorithm over LMP algorithm in Gaussian noise environments, and inferiority in uniformly distributed noise environments.
Adaptive Filtering by L. Morales