Research Article
A Neural Network Assisted FuLMS Algorithm for Active Noise Control System
@INPROCEEDINGS{10.1007/978-3-031-67162-3_26, author={Liang Jiang and Hongqing Liu and Liming Shi and Yi Zhou}, title={A Neural Network Assisted FuLMS Algorithm for Active Noise Control System}, proceedings={Communications and Networking. 18th EAI International Conference, ChinaCom 2023, Sanya, China, November 18--19, 2023, Proceedings}, proceedings_a={CHINACOM}, year={2024}, month={8}, keywords={Active Noise Control Filtered-u Least Mean Squares Algorithm Equation Error Method Neural Network Infinite Impulse Response Filter}, doi={10.1007/978-3-031-67162-3_26} }- Liang Jiang
Hongqing Liu
Liming Shi
Yi Zhou
Year: 2024
A Neural Network Assisted FuLMS Algorithm for Active Noise Control System
CHINACOM
Springer
DOI: 10.1007/978-3-031-67162-3_26
Abstract
In active noise control (ANC) systems, the Filtered-u Least Mean Square (FuLMS) algorithm has better control performance and faster rate of convergence than Filtered-x Least Mean Square (FxLMS) algorithm. However, due to the instability of adaptive infinite impulse response (IIR) filters, the application of FuLMS algorithm is not as extensive as that of FxLMS algorithm using adaptive finite impulse response (FIR) filters. The Equation Error (EE) method for adaptive IIR filtering can solve stability issues caused by poles, so in this paper we use the EE-based adaptive IIR filter to improve the FuLMS algorithm. Moreover, in this work, we introduce a neural network assisted method for designing adaptive IIR filters coefficients for the use in ANC systems. The results, compared with the FuLMS algorithm and neural network only approach, demonstrate the effectiveness of this scheme.
