.png){kind=icon}
返回
Robust spline adaptive filtering based on accelerated gradient learning: Design and performance analysis
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2021.107965.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper proposes a novel spline adaptive filtering (SAF) algorithm for nonlinear system identification under impulsive noise environments. This algorithm combines the logarithmic hyperbolic cosine (LHC) cost function and the modified Nesterov accelerated gradient (MNAG) learning method, which is called the SAF-LHC-MNAG algorithm. The LHC cost function can reduce the sensitivity of SAF to large outliers and improve the robustness to impulsive noises. Additionally, the MNAG method can further accelerate the convergence under the premise of low steady-state error. Performance analysis of this algorithm is carried out and supported by simulations. Numerical results show that the SAF-LHC-MNAG algorithm has better convergence performance than some existing SAF algorithms. Besides, experimental results confirm the effectiveness of SAF-LHC-MNAG for the accurate identification of nonlinear hysteresis system. (C) 2021 Elsevier B.V. All rights reserved.
Keyword:
Impulsive noise
Nesterov accelerated gradient
Nonlinear system identification
Spline adaptive filtering
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
10.0K
被引数:
1.7W
机构
引用论文
SIGNAL-PROCESSING WITH FRACTIONAL LOWER ORDER MOMENTS - STABLE PROCESSES AND THEIR APPLICATIONS.png)
PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
分享收藏分享收藏Functional rearrangement of 3-Cl or 3,3-diCl-γ-lactams bearing a secondary 1-chloroalkyl substituent at C-4Tetrahedron
TetrahedronIF0
PREAI
分享收藏
查看更多内容