2020
DOI: 10.48550/arxiv.2009.01978
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Including steady-state information in nonlinear models: an application to the development of soft-sensors

Abstract: When the dynamical data of a system only convey dynamic information over a limited operating range, the identification of models with good performance over a wider operating range is very unlikely. To overcome such a shortcoming, this paper describes a methodology to train models from dynamical data and steady-state information, which is assumed available. The novelty is that the procedure can be applied to models with rather complex structures such as multilayer perceptron neural networks in a bi-objective fa… Show more

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