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Deep Learning for Soft Sensor Design

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Development and Analysis of Deep Learning Architectures

Part of the book series: Studies in Computational Intelligence ((SCI,volume 867))

Abstract

Soft Sensors are mathematical models used to predict the behavior of real systems. They are usefully applied to estimate hard-to-measure quantities in the process industry. Many Soft Sensors are designed by using data-driven approaches and exploiting historical databases. Machine learning is widely used for this aim. Here, the potentialities of deep learning in solving some challenges raising in industrial applications are introduced. More specifically, the paper focuses on three specific aspects: labelled data scarcity, computational complexity reduction, and unsupervised feature exploitation. The state of the art of Soft Sensors based on deep learning is described. Then, the focus is on Soft Sensors based on Deep Belief Networks, as a research field that the authors have been investigating since years. The improvements offered by Deep Belief Networks, over more conventional data-driven approaches, in designing Soft Sensors for real-world applications will be shown. Soft Sensors for specific cases study are described.

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Author information

Authors and Affiliations

  1. DIEEI, University of Catania, Catania, Italy

    Salvatore Graziani

  2. Department of Engineering, University of Messina, Messina, Italy

    Maria Gabriella Xibilia

Authors
  1. Salvatore Graziani
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  2. Maria Gabriella Xibilia
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Corresponding author

Correspondence to Salvatore Graziani .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  2. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

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Graziani, S., Xibilia, M.G. (2020). Deep Learning for Soft Sensor Design. In: Pedrycz, W., Chen, SM. (eds) Development and Analysis of Deep Learning Architectures. Studies in Computational Intelligence, vol 867. Springer, Cham. https://doi.org/10.1007/978-3-030-31764-5_2

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