System identification is concerned with the estimation of a system on the basis of observed data. This involves specification of the model structure, estimation of the unknown model parameters, and validation of the resulting model. Least squares and maximum likelihood methods are discussed, for stationary processes (without inputs) and for input-output systems.
KeywordsAutoregressive Model Unbiased Estimator Orthogonality Condition White Noise Process Well Linear Unbiased Estimator
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