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The criterion is also employed in the model identification in this study.
Step response test is widely practiced for model identification in process industry.
This paper presents a performance assessment of gene expression programming for automatic circuit model identification in impedance spectroscopy.
The methodology for plant model identification in closed loop will be briefly surveyed and illustrated by experimental results.
Significant applications are discussed in parameter and structure optimisation for controller design and model identification, in addition to fault diagnosis, reliable systems, robustness analysis, and robot control.
Ill-conditioned processes often produce data of low quality for model identification in general, and for subspace identification in particular, because data vectors of different outputs are typically close to collinearity, being aligned in the "strong" direction.
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The results show a great potential for automated model identification methods in predictive toxicology.
The model identification methods in the design are the least squares error using QR decomposition and least squares error using singular value decomposition.
This paper discusses the importance and relevance of direct continuous-time system identification and how this relates to the solution for model identification problems in practical applications.
A general model identification algorithm, in which different noise margins and copulas corresponding to different classes are selected in given families and estimated in an automated way, from the sole observed process, is proposed.
The role of data prefiltering in model identification and validation is presented in this paper.
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