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Nonlinear empirical modeling techniques
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DOI：10.1016/j.compchemeng.2006.05.028.png)
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Abstract
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One of the key enabling technologies for computer-based process control is dynamic model development. This problem can be approached from several different perspectives and this survey focuses on one of them: the empirical development of nonlinear, discrete-time dynamic models. Critical issues considered here include the formulation of multivariable problems, the range of popular model representations available and their practical implications for model development, the selection of useful identification inputs, the utility of constraints and regularization in parameter estimation, the treatment of data anomalies and the comparative assessment of modeling results. (c) 2006 Elsevier Ltd. All rights reserved.
Keywords:
nonlinear discrete-time dynamic models
empirical model identification
multivariable models
nonlinear input-output models
nonlinear state-space models
subspace-based methods
data cleaning filters
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