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Research on Confirmation of Tension Leveller Basic Technological Parameters based on Neural Network and Genetic Algorithm

In: Computational Mechanics

Author

Listed:
  • Kai Liu

    (Xi’an University of Technology, School of Mechanical and Precision Instrument Engineering)

  • Hongzhe Xu

    (Xi’an University of Technology, School of Mechanical and Precision Instrument Engineering)

  • He Gao

    (Xi’an Jiaotong University, School of Electronics and Information Engineering)

  • Xiaohui Peng

    (Xi’an Jiaotong University, School of Electronics and Information Engineering)

  • Le Yao

    (Xi’an Jiaotong University, School of Electronics and Information Engineering)

Abstract

Confirmation of tension leveller basic technological parameters is the most important factor of leveling strip. Up to now, most factories use experts’ experience to decide these parameters, without any established rule to follow. For better quality of strip, a valid method is needed to decide technological parameters precisely and reasonably. This paper uses a method, based on neural network and genetic algorithm, to solve this problem. Neural network has a good ability to extract rules from work process of tension leveller. Then using neural network, which has learned from a lot of working swatch, to be the evaluation of fitness, genetic algorithm could easily find the best or better technological parameters. At the end of this paper, examinations are given to show the effect of this method.

Suggested Citation

  • Kai Liu & Hongzhe Xu & He Gao & Xiaohui Peng & Le Yao, 2007. "Research on Confirmation of Tension Leveller Basic Technological Parameters based on Neural Network and Genetic Algorithm," Springer Books, in: Computational Mechanics, pages 422-422, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-75999-7_222
    DOI: 10.1007/978-3-540-75999-7_222
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