Chaos Control of LMSER Principal Component Analysis Learning Algorithm

摘要：

LMSER (least mean square error reconstruction) PCA (principal component analysis) algorithm is a learning algorithm which is generally used to extract principal components of data. However, the algorithm can produce complicated dynamical behavior under certain conditions, such as the periodic oscillation, bifurcation and chaos. This paper introduces the chaos control of LMSER PCA , and the stability transformation method(STM) of chaos feedback control is specifically applied to the convergence control of LMSER PCA. Time series diagrames, Lyapunov exponent of discrete dynamical system of PCA illustrate that the desired fixed points of iterative map of LMSER PCA can be captured, and the chaotic behavior of LMSER PCA can be controlled.

作者: Lin Zuo Zhang Yi Jiancheng Lv

作者单位: School of Computer Science and Engineering, University of Electronic Science and Technology of China Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610065, P.

会议类型: 国际会议

会议名称: 2010 International Conference on Communications,Circuits and Systems(2010年通信、电路与系统国际会议)

会议地点: 成都

会议语种:英文

页码: 470-474

在线出版日期: 2010-06-28（万方平台首次上网日期，不代表论文的发表时间）