Nonlinear Principal Component Analysis Using Strong Tracking Filter

• 作 者: 丁子哲 张贤达 朱孝龙
• 机 构: Tsinghua National Laboratory for Information Science and Technology Department of Automation Tsingh
• 出 处: Tsinghua Science and Technology
• 发表日期: 2007年06期
• 关键词: Tracking filtering satisfactory powerful recursive quickly adjustment updated bl

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 devel- oped, 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 least- squares 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 ca- pability than the Kalman filtering algorithm and recursive least-squares algorithm.

An adaptive strong tracking filter (STF) based algorithm was devel- oped, which is immune to system model mismatches. Simulations demonstrate that the algorithm converges quickly and has steady steady-state accuracy. The Kalman filtering algorithm and the recursive least- squares 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 ca-pability than the Kalman filtering algorithm and recursive least-squares algorithm.