Spline Adaptive Filtering based on Normalised Least Mean Square Algorithm
Article Sidebar
Main Article Content
Abstract
This paper presents the spline adaptive filtering based on normalized least mean square (NLMS) algorithm. The proposed spline adaptive filtering can improve and develop their coefficient vectors, which can converge to optimum values. The basic theory of spline adaptive filtering is based on adaptive NLMS algorithm in comparison with the conventional adaptive least mean square (LMS) algorithm. Adaptive step-size algorithm is derived with an adaptive averaging algorithm. Simulation results show that the proposed NLMS based on spline adaptive filtering with adaptive averaging step-size algorithm can reduce the estimated error rate of proposed NLMS algorithm lower than the conventional LMS algorithm and converge dramatically to the steady-state.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright @2021 Engineering Transactions
Faculty of Engineering and Technology
Mahanakorn University of Technology
References
M. Scarpiniti, D. Comminiello, R. Parisi, A. Uncini, “Comparison of Hammerstein and Wiener systems for nonlinear acoustic echo cancelers in reverberant environments”, in Proc. International Conference on Digital Signal Processing (DSP’2011), Corfu, Greece, pp. 1–6, 2011.
V. Patel, N.V. George, “Nonlinear active noise control using spline adaptive filters”, Applied Accoustics, vol. 93, pp. 38–43, 2015.
K.J. Hunt, M. Munih, N.N. Donaldson, F.M.D. Barr, “Investigation of the Hammerstein hypothesis in the modeling of electrically stimulated muscle”, IEEE Transactions on Biomedical Engineering, vol. 45, no. 8, pp. 99–1009, 1998.
Z. Zhu, H. Leung, “Adaptive identification of nonlinear systems with application to chaotic communications”, IEEE Transactions on Circuits and Systems—I: Fundamental Theory and Applications, vol. 47, no. 7, pp. 1072–1080, 2000.
M. Scarpiniti, D. Comminiello, R. Parisi and A. Uncini, “Nonlinear spline adaptive filtering”, Signal Processing, vol. 93, Issue. 4, pp. 772-783, 2013.
C. Liu , Z. Zhang , X. Tang, “Sign normalised spline adaptive filtering algorithms against impulsive noise”, Signal Processing, vol. 148, Issue. 6, pp. 234-240, 2018.
M. Scarpiniti, D. Comminiello, R. Parisi and A. Uncini, “Novel cascade spline architectures for the identification of nonlinear systems”, IEEE Transactions on Circuits and Systems I, vol. 62, Issue. 7, pp. 1825-1835, 2015.
S. Guan, Z. Li, “Normalised spline adaptive filtering algorithm for nonlinear system identification”, Neural Processing Letter, vol. 5, pp. 1-13, 2017.
C. Liu and Z. Zhang, “Set-membership normalised least M-estimate spline adaptive filtering algorithm in impulsive noise”, Electronics Letters, vol. 54, no. 6, pp. 393-395, 2018.
S. Sitjongsataporn and P. Yuvapoositanon, “Low Complexity Adaptive Step-Size Filtered Gradient-based Per-Tone DMT Equalisation”, in Proc. IEEE International Symposium on Circuits and Systems (ISCAS), Paris, France, pp. 2526-2529, May 2010.
A. Saenmuang and S. Sitjongsataporn, “Convergence and Stability Analysis of Spline Adaptive Filtering based on Adaptive Averaging Step-size Normalised Least Mean Square Algorithm, International Journal of Intelligent Engineering & System (IJIES), vol. 13, no. 2, pp. 267-277, 2020.
S. Sitjongsataporn, W. Chimpat, “Adaptive Step-size Normalised Least Mean Square Algorithm for Spline Adaptive Filtering”, in Proc. IEEE International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), pp. 544-547, 2019.