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Fuzzy Neural Model for Flatness Pattern Recognition

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【Author】 JIA Chun-yu,SHAN Xiu-ying,LIU Hong-min,NIU Zhao-ping(School of Mechanical Engineering,Yanshan University,Qinhuangdao 066004,Hebei,China)

【摘要】 For the problems occurring in a least square method model,a fuzzy model,and a neural network model for flatness pattern recognition,a fuzzy neural network model for flatness pattern recognition with only three-input and three-output signals was proposed with Legendre orthodoxy polynomial as basic pattern,based on fuzzy logic expert experiential knowledge and genetic-BP hybrid optimization algorithm.The model not only had definite physical meanings in its inner nodes,but also had strong self-adaptability,anti-interference ability,high recognition precision,and high velocity,thereby meeting the demand of high-precision flatness control for cold strip mill and providing a convenient,practical,and novel method for flatness pattern recognition.

【Abstract】 For the problems occurring in a least square method model,a fuzzy model,and a neural network model for flatness pattern recognition,a fuzzy neural network model for flatness pattern recognition with only three-input and three-output signals was proposed with Legendre orthodoxy polynomial as basic pattern,based on fuzzy logic expert experiential knowledge and genetic-BP hybrid optimization algorithm.The model not only had definite physical meanings in its inner nodes,but also had strong self-adaptability,anti-interference ability,high recognition precision,and high velocity,thereby meeting the demand of high-precision flatness control for cold strip mill and providing a convenient,practical,and novel method for flatness pattern recognition.

【基金】 Item Sponsored by National Natural Science Foundation of China and Shanghai Baosteel Group Co(50675186);Provincial Natural Science Foundation of Hebei Province of China(E2006001038)
  • 【文献出处】 Journal of Iron and Steel Research(International) ,钢铁研究学报(英文版) , 编辑部邮箱 ,2008年06期
  • 【分类号】TP273.4
  • 【被引频次】44
  • 【下载频次】160
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