Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Analysis of the Sign Regressor Least Mean Fourth Adaptive Algorithm | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011 Article ID 373205 12 pages doi 2011 373205 Research Article Analysis of the Sign Regressor Least Mean Fourth Adaptive Algorithm Mohammed Mujahid Ulla Faiz Azzedine Zerguine EURASIP Member and Abdelmalek Zidouri Electrical Engineering Department KingFahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Correspondence should be addressed to Azzedine Zerguine azzedine@ Received 25 June 2010 Accepted 5 January 2011 Academic Editor Stephen Marshall Copyright 2011 Mohammed Mujahid Ulla Faiz et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. A novel algorithm called the signed regressor least mean fourth SRLMF adaptive algorithm that reduces the computational cost and complexity while maintaining good performance is presented. Expressions are derived for the steady-state excess-mean-square error EMSE of the SRLMF algorithm in a stationary environment. A sufficient condition for the convergence in the mean of the SRLMF algorithm is derived. Also expressions are obtained for the tracking EMSE of the SRLMF algorithm in a nonstationary environment and consequently an optimum value of the step-size is obtained. Moreover the weighted variance relation has been extended in order to derive expressions for the mean-square error MSE and the mean-square deviation MSD of the proposed algorithm during the transient phase. Computer simulations are carried out to corroborate the theoretical findings. It is shown that there is a good match between the theoretical and simulated results. It is also shown that the SRLMF algorithm has no performance degradation when compared with the least mean fourth LMF algorithm. The results in this study emphasize the usefulness of this algorithm in applications .
