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A Practical Application of a Learning Classifier System in a Steel Hot Strip Mill

  • W. Browne
  • K. Holford
  • C. Moore
  • J. Bullock
Conference paper

Abstract

The aim of this project is to improve the quality and consistency of coiling in a steel hot strip mill at British Steel Strip Products, Integrated Works. The artificial intelligence paradigm of learning classifier systems (LCS) is proposed for the processing of plant data. Improvements to a basic LCS, that allow operation on industrial data, are detailed. Initial experimental results show that the technique of LCS has the potential to become a very useful tool for processing industrial data. The stochastic computational technique will produce off- line rules to aid operator and engineering decision making. Improvements in availability, coil presentation and ultimately customer satisfaction will result in cost benefit to British Steel Plc.

Keywords

Genetic Algorithm Rule Base Learn Classifier System Integrate Work Industrial Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    A.H. Gilbert et al. Adaptive learning of process control and profit optimization using a classifier system. Evolutionary Computation, 3(2):177–198, 1995.CrossRefGoogle Scholar
  2. [2]
    L.B. Booker et al. Classifier systems and genetic algorithms. Artificial Intelligence, pages 235–282, 1989.Google Scholar
  3. [3]
    D.E. Goldberg. Genetic Algorthims in Search Optimization and Machine Learning. Addison Wesley, 1989.Google Scholar
  4. [4]
    J.J. Grefenstette. The evolution of strategies for multi-agent enviroments. Adaptive Behaviour, 1(1):65–90, 1992.CrossRefGoogle Scholar
  5. [5]
    J.H. Holland. Adaptation in Natural and Artificial. University of Michigan Press, 1975 and 1992.Google Scholar
  6. [6]
    A.J. Keane. Genetic Algorthims in Design. IEE Press, 1996.Google Scholar
  7. [7]
    S.W. Wilson. ZCS: A zeroth level classifier system. Evolutionary Computation, 2(1):1–18, 1994.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 1998

Authors and Affiliations

  • W. Browne
    • 1
  • K. Holford
    • 2
  • C. Moore
    • 2
  • J. Bullock
    • 1
  1. 1.Welsh Technology CentreBritish Steel Strip ProductsPort TalbotUK
  2. 2.University of WalesCardiffUK

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