Fast adaptive principal component extraction based on a generalized energy function  

作　　者：欧阳缮 保铮 廖桂生 

机构地区：[1]National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China [2]Department of Communication and Information Engineering, Guilin University of Electronic Technology, Guilin 541004, China

出　　处：《Science in China(Series F)》2003年第4期250-261,共12页中国科学（F辑英文版）

基　　金：supported in part by the National Natural Science Foundation of China(Grant Nos.60172011 and 69831040);Guangxi Natural Science Foundation(Grant No.gzk0007011);the Science Foundation of Guangxi Education Bureau,China

摘　　要：By introducing an arbitrary diagonal matrix, a generalized energy function (GEF) is proposed for searching for the optimum weights of a two layer linear neural network. From the GEF, we derive a recur- sive least squares (RLS) algorithm to extract in parallel multiple principal components of the input covari- ance matrix without designing an asymmetrical circuit. The local stability of the GEF algorithm at the equilibrium is analytically verified. Simulation results show that the GEF algorithm for parallel multiple principal components extraction exhibits the fast convergence and has the improved robustness resis- tance to the eigenvalue spread of the input covariance matrix as compared to the well-known lateral inhi- bition model (APEX) and least mean square error reconstruction (LMSER) algorithms.By introducing an arbitrary diagonal matrix, a generalized energy function (GEF) is proposed for searching for the optimum weights of a two layer linear neural network. From the GEF, we derive a recur- sive least squares (RLS) algorithm to extract in parallel multiple principal components of the input covari- ance matrix without designing an asymmetrical circuit. The local stability of the GEF algorithm at the equilibrium is analytically verified. Simulation results show that the GEF algorithm for parallel multiple principal components extraction exhibits the fast convergence and has the improved robustness resis- tance to the eigenvalue spread of the input covariance matrix as compared to the well-known lateral inhi- bition model (APEX) and least mean square error reconstruction (LMSER) algorithms.

关 键 词：linear neural networks principal component analysis generalized energy function recursive least squares (RLS) algorithm stability analysis. 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]