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Fuzzy Shape Control Based on Elman Dynamic Recursion Network Prediction Model

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Abstract

In the strip rolling process, shape control system possesses the characteristics of nonlinearity, strong coupling, time delay and time variation. Based on self-adapting Elman dynamic recursion network prediction model, the fuzzy control method was used to control the shape on four-high cold mill. The simulation results showed that the system can be applied to real time on line control of the shape.

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References

  1. ZHANG Ji-li, QU Jin-ping, SUN De-xing, et al. The Method of the Real-Time Multi-Step Prediction and Control of BP Network for the Delayed-Time System [J]. J of Harbin Institute of Technology, 2000, 7(2) 82–86.

    Google Scholar 

  2. LIU Bao-kun, WANG Hui, CAO Ming, et al. Direct Optimizing Predictive Control Based on Neural Network [J]. Information and Control, 1998, 27(5): 386–389 (in Chinese).

    Google Scholar 

  3. LIN Mao-qiong, CHEN Zeng-qiang, YUAN Zhu-zhi. Self-Tuning Control for Neural Network Predictive Deviation Compensation Based on Damped Least Square [J]. Information and Control, 2000, 29 (1): 27–33 (in Chinese).

    MathSciNet  Google Scholar 

  4. LIU Xi-mei, YU Fei. Predictive Control Based on Neural Networks [J]. Journal of Qingdao Institute of Chemical Technology, 1999, 20(3): 284–287 (in Chinese).

    Google Scholar 

  5. TAN Yong-hong. Self-Adaption Control Based on BP Neural Network [J]. Control Theory and Application, 1994, 11(1): 84–88 (in Chinese).

    MathSciNet  Google Scholar 

  6. FAN Yu-hong, REN Chang-ming. Research of Predictive Control Based on B-P Networks for Nonlinear System [J]. Journal of Tianjin University, 1999, 32(6): 720–723 (in Chinese).

    Google Scholar 

  7. WANG Chang-hong, XU Li-xin. A Local Recursion Neural Network Model and Its Application for Identification of Dynamic System [J]. Journal of Harbin Institute of Technology, 1998, 30(4): 21–24 (in Chinese).

    Google Scholar 

  8. LIAN Jia-chuang. Shape Control Theory and Foundation [J]. Journal Northeast Heavy Machinery Institute, 1978, 1(1): 1–20 (in Chinese).

    Google Scholar 

  9. ZHANG Jun-zhe. ASEA Cold Strip Flatness Control System [J]. Metallurgical Industry Automation, 1990, 14(2): 23–29 (in Chinese).

    MathSciNet  Google Scholar 

  10. FENG Dong-qing, XIE Song-he. Fuzzy Intelligent Control [M]. Beijing: Chemical Industry Press, 1998 (in Chinese).

    Google Scholar 

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Authors and Affiliations

  1. College of Mechanical Engineering, Yanshan University, Qinhuangdao, Hebei, 066004, China

    Chun-yu Jia (Doctor, Associate Professor) & Hong-min Liu

Authors
  1. Chun-yu Jia
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  2. Hong-min Liu
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Correspondence to Chun-yu Jia.

Additional information

Foundation Item: Item Sponsored by Provincial Natural Science Foundation of Hebei Province of China (E2004000206)

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Jia, Cy., Liu, Hm. Fuzzy Shape Control Based on Elman Dynamic Recursion Network Prediction Model. J. Iron Steel Res. Int. 13, 31–35 (2006). https://doi.org/10.1016/S1006-706X(06)60022-8

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  • DOI: https://doi.org/10.1016/S1006-706X(06)60022-8

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