A Variable Step Size of the LMS Algorithm for System Identification
Keywords:
LMS algorithm, step-size learning parameters, system identificationAbstract
The Least Mean Square (LMS) algorithm and adaptive filter techniques have been used in system identification. However, their effectiveness may degrade due to the learning gain in the LMS algorithm is a constant. In this paper, a variable step-size learning parameter (VSSP) is proposed to improve the performance of the unknown system identification. The proposed algorithm is implemented without any offline learning phase, while faster convergence can be achieved. Moreover, the computational complexities of different methods are compared. Comparative simulation results demonstrate the validity of the proposed method.
Downloads
References
Widrow, B., and S.D. Stearns, Adaptive Signal Processing. 1985: Prentice-Hall.
Bismor, D., Simulations of partial update LMS algorithms in application to active noise control. Procedia Computer Science 2016. 80: p. 1180-1190.
Yu, J., et al., An Investigation of Real-Time Active Noise Control for 10 kV Substation Noise Suppression. Sustainability, 2023. 15(18): p. 13430.
Gil, C., G. Díaz, and M. Castro. Fingerprint identification in LMS and its empirical analysis of engineer students' views. in IEEE EDUCON 2010 Conference. 2010. IEEE.
Chen, Z., L. Peng, and H. Fu. Isolated forest-based ZigBee device identification using adaptive filter coefficients. in 2022 7th International Conference on Computer and Communication Systems (ICCCS). 2022. IEEE.
Kwong, R.H. and E.W. Johnston, A variable step size LMS algorithm. IEEE Transactions on Signal Processing, 1992. 40(7): p. 1633-1642.
Aboulnasr, T. and K. Mayyas, A robust variable step-size LMS-type algorithm: analysis and simulations. IEEE Transactions on Signal Processing, 1997. 45(3): p. 631-639.
Huang, B., et al., A variable step-size FXLMS algorithm for narrowband active noise control. IEEE Transactions on Audio, Speech, and Language Processing, 2013. 21(2): p. 301-312.
Chang, D.C. and F.T. Chu, Feedforward Active Noise Control With a New Variable Tap-Length and Step-Size Filtered-X LMS Algorithm. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2014. 22(2): p. 542-555.
Khalili, A., et al., Derivation and analysis of incremental augmented complex least mean square algorithm. IET Signal Processing 2015. 9(4): p. 312-319.
Garanayak, P. and G. Panda, Fast and accurate measurement of harmonic parameters employing hybrid adaptive linear neural network and filtered‐x least mean square algorithm. IET Generation, Transmission 2016. 10(2): p. 421-436.
Published
How to Cite
Issue
Section
Categories
Copyright (c) 2024 Nguyen Le Thai, Hoang Hai Son
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.