Nonlinear Principal Component Analysis Using Strong Tracking Filter  

作　　者：丁子哲 张贤达 朱孝龙 

机构地区：[1]Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University [2]Alcatel Shanghai Bell Co. Ltd.

出　　处：《Tsinghua Science and Technology》2007年第6期652-657,共6页清华大学学报（自然科学版（英文版）

基　　金：Supported by the Basic Research Foundation of Tsinghua National Laboratory for Information Science and Technology (TNList) ;the National Natural Science Foundation of China (No. 60675002)

摘　　要：The paper analyzes the problem of blind source separation （BSS） based on the nonlinear principal component analysis （NPCA） criterion. An adaptive strong tracking filter （STF） based algorithm was developed, which is immune to system model mismatches. Simulations demonstrate that the algorithm converges quickly and has satisfactory steady-state accuracy. The Kalman filtering algorithm and the recursive leastsquares type algorithm are shown to be special cases of the STF algorithm. Since the forgetting factor is adaptively updated by adjustment of the Kalman gain, the STF scheme provides more powerful tracking capability than the Kalman filtering algorithm and recursive least-squares algorithm.The paper analyzes the problem of blind source separation （BSS） based on the nonlinear principal component analysis （NPCA） criterion. An adaptive strong tracking filter （STF） based algorithm was developed, which is immune to system model mismatches. Simulations demonstrate that the algorithm converges quickly and has satisfactory steady-state accuracy. The Kalman filtering algorithm and the recursive leastsquares type algorithm are shown to be special cases of the STF algorithm. Since the forgetting factor is adaptively updated by adjustment of the Kalman gain, the STF scheme provides more powerful tracking capability than the Kalman filtering algorithm and recursive least-squares algorithm.

关 键 词：nonlinear principal component analysis strong tracking filter recursive least-squares 

分 类 号：TN713[电子电信—电路与系统]