TY - JOUR
T1 - Design of delayless multi-sampled subband functional link neural network with application to active noise control
AU - Zhang, Sheng
AU - Zheng, Wei Xing
AU - Han, Hongyu
N1 - Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2023/1
Y1 - 2023/1
N2 - To accelerate the convergence speed of the functional link neural network (FLNN) particularly for colored input signals, this paper proposes a delayless multi-sampled multiband-structured subband FLNN (DMSFLNN) structure. Then, to update the weights of the DMSFLNN, a normalized subband adaptive algorithm is devised. Next, the stability conditions, optimal step size and computational complexity are investigated. Moreover, the proposed method is applied to the nonlinear active noise control, obtaining the delayless multi-sampled multiband-structured filtered-s normalized least mean square (DMSFsNLMS) algorithm. Finally, simulation results demonstrate that the proposed method improves the convergence speed of the FLNN.
AB - To accelerate the convergence speed of the functional link neural network (FLNN) particularly for colored input signals, this paper proposes a delayless multi-sampled multiband-structured subband FLNN (DMSFLNN) structure. Then, to update the weights of the DMSFLNN, a normalized subband adaptive algorithm is devised. Next, the stability conditions, optimal step size and computational complexity are investigated. Moreover, the proposed method is applied to the nonlinear active noise control, obtaining the delayless multi-sampled multiband-structured filtered-s normalized least mean square (DMSFsNLMS) algorithm. Finally, simulation results demonstrate that the proposed method improves the convergence speed of the FLNN.
UR - https://hdl.handle.net/1959.7/uws:68544
U2 - 10.1016/j.sigpro.2022.108757
DO - 10.1016/j.sigpro.2022.108757
M3 - Article
SN - 0165-1684
VL - 202
JO - Signal Processing
JF - Signal Processing
M1 - 108757
ER -