1384376

11159

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.

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Maximum likelihood estimate , Principal component analysis , Process identi®cation , Least squares

Studia Iranica

33

33