Title :
A neurocomposition method for extraction of principal components of stochastic processes
Author :
Chen, Hong ; Liu, Ruey-wen
Author_Institution :
Dept. of Electr. Eng., Notre Dame Univ., IN, USA
Abstract :
A neurocomputation method with an APEX (adaptive principal components extraction) algorithm has recently been proposed by S.Y. Kung and K.I. Diamantaras (1990). In the present work, an improved method with an algorithm called OPEX is presented. It was shown by simulation that OPEX is more robust and has a shorter convergence time than APEX when small eigenvalues are present and the autocorrelation matrix of the input process is ill conditioned
Keywords :
convergence of numerical methods; eigenvalues and eigenfunctions; mathematics computing; neural nets; stochastic processes; APEX; OPEX; autocorrelation matrix; convergence time; eigenvalues; neural nets; neurocomposition; neurocomputation; stochastic process principal components extraction; Autocorrelation; Convergence; Data compression; Eigenvalues and eigenfunctions; Hebbian theory; Neural networks; Pattern recognition; Robust control; Robustness; Stochastic processes; Symmetric matrices;
Conference_Titel :
Decision and Control, 1991., Proceedings of the 30th IEEE Conference on
Conference_Location :
Brighton
Print_ISBN :
0-7803-0450-0
DOI :
10.1109/CDC.1991.261076
