| 基于量子粒子群算法的BP网络板形模式识别研究 |
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| 引用本文： | 许东杰,贾春玉,崔艳超,叶亚宁.基于量子粒子群算法的BP网络板形模式识别研究[J].东北重型机械学院学报,2011(1):35-39. |
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| 作者姓名： | 许东杰 贾春玉 崔艳超 叶亚宁 |
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| 作者单位： | 燕山大学机械工程学院,河北秦皇岛066004 |
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| 摘 要： | 针对目前板形模式识别方法存在的问题,以及考虑到现代轧机板形控制手段的多样化和板形控制能力的提高,为了提高板形模式识别模型的精度,本文以1次、2次、3次和4次勒让德正交多项式为板形基本模式,建立了基于量子粒子群-BP算法混合优化神经网络的新型板形模式识别模型。仿真实验表明,该模型抗干扰能力强、识别精度高、速度快,可以为板形控制策略的制定提供可靠依据。
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| 关 键 词： | 板形 模式识别 勒让德多项式 量子粒子群算法 BP神经网络 |
Study on BP network flatness pattern recognition based on quantum particle swarm optimization algorithm |
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| Authors: | XU Dong-jie JIA Chun-yu CUI Yan-chao YE Ya-ning |
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| Affiliation: | (College of Mechanical Engineering,Yanshan University,Qinhuangdao,Hebei 066004,China) |
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| Abstract: | In the light of the problems existed in the present flatness pattern recognition method,and considering the practical situation of modern mills with many different flatness control means and the improvement of flatness control capability,in order to improve the precision of flatness pattern recognition model,in this paper,a new flatness pattern recognition model is established based on QPSO-BP algorithm mix optimization neural network.This model takes linear,quadratic,cubic and biquadratic Legendre orthogonal polynomials as flatness basic patterns.The simulation experiments indicate that this model has strong anti-interference ability,high recognition precision and fast velocity,and can provide reliable basis for the formulation of flatness control strategy. |
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| Keywords: | flatness pattern recognition Legendre orthogonal polynomial QPSO algorithm BP neural network |
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