Abstract
Self-organizing maps have been extensively used for visualization of industrial processes. Nevertheless, most of these approaches lack insight about the dynamic behavior. Recently, an approach to define visualizable maps of dynamics from data has been proposed. We propose the application of this approach to single-input single-output processes by defining several maps related to relevant features in the time-response analysis. This features are commonly used in control engineering. We show that these maps are intuitive and consistent tools for knowledge discovery and validation. They also provide a general overview of the process behavior and can be used along with other previously defined maps for process analysis and monitoring.
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Prada, M.A. et al. (2010). Application of SOM-Based Visualization Maps for Time-Response Analysis of Industrial Processes. 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_47
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DOI: https://doi.org/10.1007/978-3-642-15822-3_47
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