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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:
93E12System identification (stochastic systems)
93E10Estimation and detection in stochastic control
93E25Computational methods in stochastic control
93C10Nonlinear control systems
93C35Multivariable systems, multidimensional control systems
93C55Discrete-time control systems