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- W3093948897 abstract "The objective of System Identification is to estimate models that can reproduce the static and dynamic properties of the process being modeled. Some system identification methods require a high order linear model of the process. These models are adopted as the process most reliable description. In some applications, it is necessary to reduce the order of the high order model. An important question is what is the best order of the high order model? This work presents an optimization strategy that maximizes the fit index in the search for the best high order of SISO and MIMO ARX models. The search for the best time delays related to the high order model is also addressed. The results of the optimization strategy are compared to those obtained by the exhaustive search in two scenarios: comparison of the fit indices of the high order models and comparison of these indices in models with reduced order obtained from the high order models." @default.
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- W3093948897 date "2021-01-01" @default.
- W3093948897 modified "2023-10-14" @default.
- W3093948897 title "Optimal order selection for high order ARX models" @default.
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- W3093948897 doi "https://doi.org/10.1016/j.dsp.2020.102897" @default.
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