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Orthogonal least squares methods and their application to non-linear system identification. (English) Zbl 0686.93093

Summary: 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.

MSC:

93E12 Identification in stochastic control theory
93E10 Estimation and detection in stochastic control theory
93E25 Computational methods in stochastic control (MSC2010)
93C10 Nonlinear systems in control theory
93C35 Multivariable systems, multidimensional control systems
93C55 Discrete-time control/observation systems
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