Abstract
In this paper a new method based on the self-organizing map (SOM) is proposed to track and identify changes in the dynamic behaviour of a physical process. In a first stage, a SOM is trained on a parameter space composed of the coefficients of local dynamic models estimated around different operating points of the process. On execution, new models estimated from process data are compared against the stored models in the SOM to yield residual models that contain relevant information about the changes in the process dynamics. This information can be efficiently represented using time-frequency visualizations, that reveal unseen patterns in the frequency response and hide those that can be explained by the model.
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Díaz, I., Cuadrado, A.A., Diez, A.B., Domínguez, M., Fuertes, J.J., Prada, M.A. (2010). Visualization of Changes in Process Dynamics Using Self-Organizing Maps. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15822-3_42
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DOI: https://doi.org/10.1007/978-3-642-15822-3_42
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