Abstract
The novel approach how to determine the past and future time horizon for prediction of plant technological parameters with missing data is described. These situations frequently happen in real life when the whole spans of time data are missing due to e.g. plant outages. For this, the mutual information analysis has been proposed to use. The hierarchical training of ANNs has been applied to reduce the error of time series forecasting.
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References
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© 2000 Springer-Verlag Berlin Heidelberg
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Hrehuš, M., Figedy, Š. (2000). Practical Approach to Prediction of Plant Technological Parameters with Missing Data. In: Sinčák, P., Vaščák, J., Kvasnička, V., Mesiar, R. (eds) The State of the Art in Computational Intelligence. Advances in Soft Computing, vol 5. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1844-4_40
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DOI: https://doi.org/10.1007/978-3-7908-1844-4_40
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1322-7
Online ISBN: 978-3-7908-1844-4
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