计算机科学
自适应滤波器
人工神经网络
过滤器组
链接(几何体)
算法
滤波器(信号处理)
拓扑(电路)
数学
计算机网络
人工智能
组合数学
计算机视觉
作者
Jianhong Ye,Yi Yu,Badong Chen,Zongsheng Zheng,Jie Chen
出处
期刊:IEEE Transactions on Circuits and Systems I-regular Papers
[Institute of Electrical and Electronics Engineers]
日期:2024-12-18
卷期号:72 (9): 4967-4980
被引量:2
标识
DOI:10.1109/tcsi.2024.3516211
摘要
Compared with the functional link neural network (FLNN) algorithm, the delayless multi-sampled multiband-structured subband FLNN (DMSFLNN) algorithm provides fast convergence when encountering highly auto-correlated input signals, but there is a compromise between convergence and steady-state performances. Therefore, in order to overcome this flaw, we develop an optimal DMSFLNN (ODMSFLNN) algorithm by minimizing the mean square deviation of the weight vector with respect to the subband gain vectors. Interestingly, a vectorized version is also proposed for the ODMSFLNN algorithm, which aims at reducing computational complexity. Additionally, this paper also presents a stability analysis of this algorithm. Then, considering the impulsive noise environment, we develop two robust variants of ODMSFLNN that are the R-ODMSFLNN-I and R-ODMSFLNN-II algorithms, which are based on the specified robust function and the energy constraint of the weight update increment, respectively. Finally, to resolve that the DMSFLNN algorithm may not exploit cross-terms of input samples in nonlinear active noise control scenarios, we further propose the subband second-order Volterra filter (SSOVF) framework in an analogy way and apply the R-ODMSFLNN-II learning principle to obtain the robust optimal SSOVF algorithm. Simulations in several nonlinear scenarios have shown that the proposed algorithms perform better than their competitors.
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