.png){kind=icon}
返回
Low-Complexity Feature Stochastic Gradient Algorithm for Block-Lowpass Systems
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2944187.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
New approaches have been proposed to detect and exploit sparsity in adaptive systems. However, the sparsity is not always explicit among the system coefficients, thus requiring some tools to reveal it. By means of the so-called feature function, we propose the low-complexity feature stochastic gradient (LF-SG) algorithm to exploit hidden sparsity. The proposed algorithm aims at reducing the computational load of the learning process, as compared to the least-mean-square (LMS) algorithm. We focus on block-lowpass systems, but the proposed approach can easily be adapted to exploit other kinds of features of the unknown system, e.g., highpass and bandpass characteristics. Then, we analyze some properties of the LF-SG algorithm, namely its steady-state mean squared error (MSE), its bias, and the choice of the step-size parameter. Simulation results illustrate the competitive MSE performance of the LF-SG in comparison with the LMS, but the former algorithm requires much fewer multiplication operations to identify lowpass systems. For instance, to identify a measured room impulse response, the LF-SG algorithm realized less than half of the multiplication operations required by the LMS algorithm.
Keyword:
Steady-state
Stochastic processes
Adaptive systems
Computational modeling
Adaptation models
Convergence
Licenses
Adaptive filtering
block-lowpass systems
computational complexity
feature
LMS
sparsity
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:
9.8W
被引数:
29.4W
机构
引用论文
Conservation of Putative Liquid-Liquid Phase Separating Proteins in Multiple Drug-Resistant Mycobacterium tuberculosis: Role in Host-Pathogen Interactions?假定的液-液相分离蛋白在多重耐药结核分枝杆菌中的保守性：其在宿主-病原体相互作用中的作用？
PREAI
分享收藏Recovery of Block-Structured Sparse Signal Using Block-Sparse Adaptive Algorithms via Dynamic Grouping.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Distributed Spectrum Estimation Based on Alternating Mixed Discrete-Continuous Adaptation基于交替混合离散-连续自适应的分布式谱估计OAAI
OAAI分享收藏A Second-Order Lowpass Parameter-Varying Filter Based on the Interconnection of First-Order Stages
PREAI
分享收藏
查看更多内容