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Generating a Rule Set for the Fiber-to-Yarn Production Process by Means of an Efficiency-based Classifier System

  • Stefan Sette
  • Lieva Van Langenhove
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 108)

Summary

One of the important production processes in the textile industry is the spinning process. Starting from cotton fibers, yarns are (usually) created on a rotor-spinning machine. The spinnability of a fiber is dependant on its quality and on the machine settings of the spinning machine. It would be a great benefit to be able to predict the spinnability and resulting strength of the yarn starting from a certain quality and from machine settings. To this end, two totally diiffferent modeling approaches can be considered: the so-called ‘white’ modeling and the so-called ‘black box’ modeling. In white modeling the process is described by mathematical equations, which are based upon (theoretical) physical knowledge of the process. Extensive physical information about the process is in this case available through physical, chemical or mechanical equations giving the user a thorough insight into the operation of the process. However, due to the large input (and output) dimensions of the fiber-to-yarn process and their complex interactions, no exact mathematical model of a spinning machine is known to exist nor is it likely that such a model will ever be constructed. A black box model, in contrast to white modeling, simply connects input parameters to the output without giving or containing any substantial physical information about the process itsellf Black box models have been successfully constructed by Pynckels et al. to predict the spinnability (Pynckels, 1995) and the characteristics (Pynckels, 1997) of the yarn using neural networks with a Backpropagation learning rule. Apart from the lack of physical information, these models also have no fault indication or measure of uncertainty about the results.

Keywords

Membership Degree Fiber Strength Fiber Quality Global Efficiency Learning Classifier System 
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. Bonnarini A. (1997a), Anytime learning and adaptation of structured fuzzy behaviors, accepted for publication on the Special Issue of the Adaptive Behavior Journal about “Complete agent learning in complex environments”, Maja Mataric (Ed.), No. 5Google Scholar
  2. Boullart L., Sette S. (1997), Genetic Algorithms: Theory & Applications, Journal A, Vol. 38, n.2, 13–23.Google Scholar
  3. Boullart L., Sette S. (1998), High Performance Learning Classifier Systems, Engineering of Intelligent Systems (EIS’ 98), International ICSC Symposium, 11–13 February, Tenerife, 249–256.Google Scholar
  4. BRITE/EURAM project BREU 00052-TT (1990–1993), ‘Research for a mathematical and rule based system which allows to optimize a cotton mixture, based on the interdependence of signifiicant fiiber properties, process parameters, yarn properties and spinning machinery performances.’Google Scholar
  5. Darwin C. (1859), The Origin of Species: by Means of Natural Selection or the Preservation of Favored Races in the struggle for Life.Google Scholar
  6. Goldberg D.E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning., Addison Wesley Publishing Company.MATHGoogle Scholar
  7. Holland J.H. (1968), Hierarchical description of universal spaces and adaptive systems, Tech. Report ORA Projects 01252 and 0826, Ann Arbor, University of Michigan, Dept. Comp. Science & Comm. ScienceGoogle Scholar
  8. Holland J.H. (1973), Genetic Algorithms and the Optimal Allocation of Trials., SIAM Journal of Computing, Vol. 2 (2), 88–105MathSciNetMATHCrossRefGoogle Scholar
  9. Holland J.H., Reitman J.S. (1978), Cognitive systems based on adaptive algorithms. In D.A. Waterman & F. Hayes-Roth (Eds.), Pattern directed inference systems (pp.313–329). New York: Academic PressGoogle Scholar
  10. Pynckels F., Sette S., Van Langenhove L., Kiekens P., Impe K. (1995), Use of Neural nets for Determining the Spinnability of Fibers, Journal of the Textile Institute, 86 (3), 425–437CrossRefGoogle Scholar
  11. Pynckels F., Kiekens P., Sette S., L. Van Langenhove, Impe K. Use of Neural Nets to Simulate the Spinning Process, The Journal of the Textile Institute, 88 (1), 440–447Google Scholar
  12. Sette S., Boullart L., Van Langenhove L., Kiekens P. (1997a), Optimizing the Fiber-to-Yarn Production Process with a combined Neural Network/Genetic Algorithm Approach, Textile Research Journal, Vol. 67, No. 2, 84–92Google Scholar
  13. Sette S., Learning systems by means of evolutionary algorithms, PhD thesis, Faculty of Applied Sciences, University Ghent, 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Stefan Sette
    • 1
  • Lieva Van Langenhove
    • 1
  1. 1.Department of TextilesUniversity of GentBelgium

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