Identification of Finite Impulse Response (FIR) filters refer to finding out the coefficients also known as the weights of its transfer function. Adaptive filtering using Least Mean Square (LMS) Algorithm is used to find the estimated weights of the transfer function, using ATMEGA16 processor. This method can be used to find the coefficients of complex resistive circuits. This is done by constantly comparing the FIR system with Adaptive filter until the difference signal is zero, both the systems are fed with same input signals.

1. Irina Dorean, Marina Topa, Botond Sandor Kirei and Erwin Szopos, “Interdisciplinarity in Engineering” Scientific International Conference, TG.MURES – ROMÂNIA, 15-16 November 2007.
2. S. A. Ghauri and M. F. Sohail, “System identification using LMS, NLMS and RLS,” 2013 IEEE Student Conference on Research and Developement, 2013, pp. 65-69, doi: 10.1109/SCOReD.2013.7002542.
3. Ibraheem, Ibraheem Kasim. “Adaptive System Identification Using LMS Algorithm Integrated with Evolutionary Computation.” arXiv preprint arXiv: 1806.01782 (2018).
4. T. Jia, Z. J. shu and W. Jie, “An Improved Variable Step Size LMS Adaptive Filtering Algorithm and Its Analysis,” 2006 International Conference on Communication Technology, 2006, pp. 1-4, doi: 10.1109/ICCT.2006.341916.
5. Yilun Chen, Y. Gu and A. O. Hero, “Sparse LMS for system identification,” 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009, pp. 3125-3128, doi: 10.1109/ICASSP.2009.4960286.
6. Q. Yan-Bin, M. Fan-Gang and G. Lei, “A New Variable Step Size LMS Adaptive Filtering Algorithm,” 2007 IEEE International Symposium on Industrial Electronics, 2007, pp. 1601-1605, doi: 10.1109/ISIE.2007.4374843.
7. R. Yu, Y. Song and S. Rahardja, “LMS in prominent system subspace for fast system identification,” 2012 IEEE Statistical Signal Processing Workshop (SSP), 2012, pp. 209-212, doi: 10.1109/SSP.2012.6319662.
8. Ching-An Lai, “NLMS algorithm with decreasing step size for adaptive IIR filters,” Signal Processing, Volume 82, Issue 10, October 2002, pp. 1305–1316, https://doi.org/10.1016/S0165-1684(02)00275-X.
9. G. Akgün, H. u. Hasan Khan, M. Hebaish and D. Gohringer, “System Identification using LMS, RLS, EKF and Neural Network,” 2019 IEEE International Conference on Vehicular Electronics and Safety (ICVES), 2019, pp. 1-6, doi:
10. 1109/ICVES.2019.8906339.
    10. Ibraheem Kasim Ibraheem, Adaptive IIR and FIR Filtering using Evolutionary LMS Algorithm in View of System Identification, International Journal of Computer Applications (0975 – 8887), Volume 182 – No. 11, August 2018.
11. S. C. Douglas, “Analysis of the multiple-error and block least-mean-square adaptive algorithms,” in IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, vol. 42, no. 2, pp. 92-101, Feb. 1995, doi: 10.1109/82.365348.
12. Geun-Taek Ryu, Dong-Won Kim, Jung-Go Choe, Dae-Sung Kim and Hyeon-Deok Bae, “Adaptive system identification using fuzzy inference based LMS algorithm,” Proceedings of Third International Conference on Signal Processing (ICSP’96), 1996, pp. 587-590 vol.1, doi: 10.1109/ICSIGP.1996.567332.
13. E. Szopos, M. Topa and N. Toma, “System identification with adaptive algorithms,” 2005 IEEE 7th CAS Symposium on Emerging Technologies: Circuits and Systems for 4G Mobile Wireless Communications, 2005, pp. 64-67, doi: 10.1109/EMRTW.2005.195681.
14. M. Nakamoto, N. Shimizu and T. Yamamoto, “A system identification approach for design of IIR digital filters,” IECON 2013 – 39th Annual Conference of the IEEE Industrial Electronics Society, 2013, pp. 2360-2365, doi: 10.1109/IECON.2013.6699500.
15. Archana Sarangi, Shubhendu kumar Sarangi, Siba Prasada Panigrahi, “An approach to identification of unknown IIR systems using crossover cat swarm optimization,” Perspectives in Science, Volume 8, 2016, Pages 301-303, Doi: 10.1016/j.pisc.2016.04.059.

Citations by Dimensions

Citations by PlumX

Crossref Citations

This paper is currently not cited.

No. of Downloads per Month

No. of Downloads per Country