Process identification based on last principal component analysis

A simple linear identification algorithm is presented in this paper. The la st principal component (LPC), the eigenvector corresponding 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-k nown principal component analysis is extended to the generalized last princ ipal component analysis (GLPC). For processes with colored measurement nois e or disturbances, consistency of the GLPC estimator is achieved without in volving iteration or non-linear numerical optimization. The proposed algori thm is illustrated by a simulated example and application to a pilot-scale process. (C) 2000 Elsevier Science Ltd. All rights reserved.