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An introduction to neural networks and their applications in manufacturing

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  • Published: December 1992
  • Volume 3, pages 391–403, (1992)
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An introduction to neural networks and their applications in manufacturing
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  • B. Wu1 
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  • 27 Citations

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Abstract

It is a frequently quoted fact that today's manufacturing functions are becoming more and more ‘inter-disciplinary’, with new approaches and techniques continuously and rapidly introduced and adopted. The recent applications of neural networks in manufacturing provide a typical example of this trend. This paper examines the structures and functions of neural networks, and provides some examples of their manufacturing applications.

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References

  • Aleksander, I., and Morton, H. (1990)An Introduction to Neural Computing, Chapman & Hall, London.

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  • Burke, L. I., and Rangwala, S. (1991) Tool condition monitoring in metal cutting: a neural network approach.Journal of Intelligent Manufacturing 2(5), 269–280.

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  • JIM (1991) Special issue: Neural networks in manufacturing.Journal of Intelligent Manufacturing,2(5).

  • Kosko, B. (1992)Neural Networks and Fuzzy Systems, Prentice Hall, Inc.

  • Malave, C., and Ramachandran, S. (1991) Neural network-based design of cellular manufacturing systems.Journal of Intelligent Manufacturing,2(5), 305–314.

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  • Spelt, P. F. and Glover, C. W. (1991) Hybrid artificial intelligence architecture for diagnosis and decision-making in manufacturing.Journal of Intelligent Manufacturing,2(5), 261–268.

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  • Wu, B. (1992)Manufacturing Systems Design and Analysis, Chapman & Hall, London.

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Authors and Affiliations

  1. Department of Manufacturing and Engineering Systems, Brunel University, UB8 3PH, Uxbridge, Middlesex, UK

    B. Wu

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  1. B. Wu
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Wu, B. An introduction to neural networks and their applications in manufacturing. J Intell Manuf 3, 391–403 (1992). https://doi.org/10.1007/BF01473534

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  • Issue Date: December 1992

  • DOI: https://doi.org/10.1007/BF01473534

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Keywords

  • artificial intelligence
  • neural network
  • error back-propagation
  • competitive learning
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