Two-Step Process Identification With Correlation Analysis and Least-Squares Parameter Estimation

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Abstract
An identification method is described which first identifies a linear nonparametric model (crosscorrelation function, impulse response) by correlation analysis and then estimates the parameters of a parametric model (discrete transfer function) and also includes a method for the detection of the model order and the time delay. The performance, the computational expense and the overall reliability of this method is compared with five other identification methods. This two-step identification method, which can be applied off-line or on-line, is especially suited to identification by process computers, since it has the properties: Little a priori knowledge about the structure of the process model; very short computation time; small computer storage; no initial values of matrices and parameters are necessary and no divergence is possible for the on-line version. Results of an on-line identification of an industrial process with a process computer are shown.

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