QQ客服 官方微博 反馈留言

	Adaptive Filtering under Minimum Information Divergence Criterion
	Chen, Badong ; Zhu, Yu ; Hu, Jinchun ; Sun, Zengqi
	2010-10-12 ; 2010-10-12
关键词	Adaptive filtering information divergence kernel method stochastic gradient algorithm STOCHASTIC-SYSTEMS ALGORITHM GRADIENT Automation & Control Systems
中文摘要	Traditional filtering theory is always based on optimization of the expected value of a suitably chosen function of error, Such as the minimum mean-square error (MMSE) criterion, the minimum error entropy (MEE) criterion, and so oil. None of those criteria could capture all the probabilistic information about the error distribution. In this work, we propose a novel approach to shape the probability density function (PDF) of the errors in adaptive filtering. As the PDF contains all the probabilistic information, the proposed approach can be used to obtain the desired variance or entropy, and is expected to be useful in the complex signal processing and learning systems. In our method, the information divergence between the actual errors and the desired errors is chosen as the cost function, which is estimated by kernel approach. Some important properties of the estimated divergence are presented. Also, for the finite impulse response (FIR) Filter, a stochastic gradient algorithm is derived. Finally, simulation examples illustrate the effectiveness of this algorithm in adaptive system training.
语种	英语 ; 英语
出版者	INST CONTROL ROBOTICS & SYSTEMS, KOREAN INST ELECTRICAL ENGINEERS ; BUCHEON ; BUCHEON TECHNO PARK 401-1506, 193 YAKDAE-DONG WONMI-GU, BUCHEON, GYEONGGI-DO 420-734, SOUTH KOREA
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/80317]
专题	清华大学
推荐引用方式 GB/T 7714	Chen, Badong,Zhu, Yu,Hu, Jinchun,et al. Adaptive Filtering under Minimum Information Divergence Criterion[J],2010, 2010.
APA	Chen, Badong,Zhu, Yu,Hu, Jinchun,&Sun, Zengqi.(2010).Adaptive Filtering under Minimum Information Divergence Criterion..
MLA	Chen, Badong,et al."Adaptive Filtering under Minimum Information Divergence Criterion".(2010).

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。