Abstract
This industrial processes monitoring based on a neural network presents low run-time, and it useful for critical time tasks with periodic processing. This method allows the time prediction in which a variable will arrive to abnormal or important values. The data of each variable are used to estimate the parameters of a continuous mathematical model. At this moment, four models are used: first-order or second-order in three types (critically damped, overdamped or underdamped). An optimization algorithm is used for estimating the model parameters for a dynamic response to step input function, because this is the most frequent disturbance. Before performing the estimation, the most appropriate model is determined by means of a feed-forward neural network.
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Sánchez-Fernández, L.P., Yáñez-Márquez, C., Pogrebnyak, O. (2006). Neural Network Based Industrial Processes Monitoring. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_136
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DOI: https://doi.org/10.1007/11760191_136
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
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