Orthogonal least squares methods and their application to non-linear system identification

Abstract

Identification algorithms based on the well-known linear least squares methods of
gaussian elimination, Cholesky decomposition, classical Gram-Schmidt, modified
Gram-Schmidt, Householder transformation, Givens method, and singular value
decomposition are reviewed. The classical Gram-Schmidt, modified Gram-Schmidt,
and Householder transformation algorithms are then extended to combine
structure determination, or which terms to include in the model, and parameter
estimation in a very simple and efficient manner for a class of multivariable
discrete-time non-linear stochastic systems which are linear in the parameters.

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