Abstract
Classification of spatially distributed measurements in industrial processes is an important but difficult task. Interpretation of the signals from horizontal probes in blast furnaces is a typical example. A neural network-based classification of such temperature measurements is described in this paper. Using temperature profiles first classified by the team, training and test sets were formed, and feedforward neural networks of different size were trained and evaluated on the material. The network size was found to clearly affect the quality of the classifications. By a judicious choice of the number of hidden nodes, a reliable neural classifier was obtained, which was incorporated in a supervision and control system for a Finnish blast furnace.
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Saxén, H., Bulsari, A., Karilainen, L., Raipala, K. (1995). Application of neural networks for classification of temperature distribution patterns. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_7
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DOI: https://doi.org/10.1007/978-3-7091-7535-4_7
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
Online ISBN: 978-3-7091-7535-4
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