Latin Abstract :
A simple linear identi®cation algorithm is presented in this paper. The last principal component (LPC), the eigenvector corre-
sponding to the smallest eigenvalue of a non-negative symmetric matrix, contains an optimal linear relation of the column vectors
of the data matrix. This traditional, well-known principal component analysis is extended to the generalized last principal compo-
nent analysis (GLPC). For processes with colored measurement noise or disturbances, consistency of the GLPC estimator is
achieved without involving iteration or non-linear numerical optimization. The proposed algorithm is illustrated by a simulated
example and application to a pilot-scale process.
NaturalLanguageKeyword :
Maximum likelihood estimate , Principal component analysis , Process identi®cation , Least squares
