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Abstract In this paper, we revisit maximum likelihood methods for identification of errors-in-variables systems. We assume that the system admits a parametric description, and that the input is a stochastic… Click to show full abstract
Abstract In this paper, we revisit maximum likelihood methods for identification of errors-in-variables systems. We assume that the system admits a parametric description, and that the input is a stochastic ARMA process. The cost function associated with the maximum likelihood criterion is minimized by introducing a new iterative solution scheme based on the expectation-maximization method, which proves fast and easily implementable. Numerical simulations show the effectiveness of the proposed method.
Keywords:
errors variables;
maximum likelihood;
variables models;
likelihood identification;
identification errors
Journal Title: IFAC-PapersOnLine
Year Published: 2017
Link to full text (if available)
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Journal Title: IFAC-PapersOnLine
Year Published: 2017
Link to full text (if available)
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recommendations!