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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.

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
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