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Dynamics in Logistics pp 465–474Cite as

Towards the Learning Behaviour and Performance of Artificial Neural Networks in Production Control

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Abstract

Recently, artificial neural networks have proven their potential in manifold production related tasks. At this, the learning ability is both their greatest strength and greatest weakness. The performance in the application is highly related to the learning process. Only sufficient training data in combination with a suitable configuration of the learning parameters leads on to useful results. Nevertheless, due to the general structure of artificial neural networks, the learning behaviour not necessarily correlates with the later functional behaviour. This paper considers the learning process and the application performance of four common network architectures in two production related applications; control and prediction. The evaluation of the network performance takes place by means of a generic shop floor model.

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Acknowledgments

This research is funded by the German Research Foundation (DFG) as part of the project Automation of continuous learning and examination of the long-run behaviour of artificial neural networks for production control, index SCHO 540/16-1.

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Authors and Affiliations

  1. BIBA—Bremer Institut für Produktion und Logistik GmbH at the University of Bremen, Hochschulring 20, 28359, Bremen, Germany

    Bernd Scholz-Reiter & Florian Harjes

Authors
  1. Bernd Scholz-Reiter
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  2. Florian Harjes
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Corresponding author

Correspondence to Florian Harjes .

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Editors and Affiliations

  1. , Department of Computer Science, University of Bremen, Linzer Straße 9a, Bremen, 28359, Germany

    Hans-Jörg Kreowski

  2. Rector of the University of Bremen, Hochschulring 20, Bremen, 28359, Germany

    Bernd Scholz-Reiter

  3. Logistik (BIBA), Planung und Steuerung, Bremer Institut für Produktion und, Hochschulring 20, Bremen, 28359, Germany

    Klaus-Dieter Thoben

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Scholz-Reiter, B., Harjes, F. (2013). Towards the Learning Behaviour and Performance of Artificial Neural Networks in Production Control. In: Kreowski, HJ., Scholz-Reiter, B., Thoben, KD. (eds) Dynamics in Logistics. Lecture Notes in Logistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35966-8_39

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  • DOI: https://doi.org/10.1007/978-3-642-35966-8_39

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35965-1

  • Online ISBN: 978-3-642-35966-8

  • eBook Packages: EngineeringEngineering (R0)

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