| 基于人工蜂群算法的Elman网络板形预测 |
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| 引用本文： | 张秀玲,赵文保,李少清,徐腾. 基于人工蜂群算法的Elman网络板形预测[J]. 沈阳大学学报：自然科学版, 2012, 24(3): 38-42 |
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| 作者姓名： | 张秀玲 赵文保 李少清 徐腾 |
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| 作者单位： | 燕山大学河北省工业计算机控制工程重点实验室,河北秦皇岛066004 国家冷轧板带装备及工艺工程技术研究中心,河北秦皇岛066004 |
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| 基金项目： | 国家自然科学基金资助项目 |
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| 摘 要： | 针对常规Elman网络泛化能力差的缺点，以及工业生产中对高精度板形预测模型的需要，用人工蜂群算法（ABC）代替误差反传算法训练Elman网络，建立了一个基于Elman网络的板形预测模型．神经网络的隐层节点数通过经验公式和仿真试验来确定．通过仿真验证，用人工蜂群算法训练的Elman网络在同等条件下比常规Elman网络具有更强的泛化能力，其板形预测精度更高．
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| 关 键 词： | 人工蜂群算法 Elman网络 板形预测模型 泛化能力 |
Flatness Prediction based on Elman Network with Artificial Bee Colony Algorithm |
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| ZHANG Xiuling,ZHAO Wenbao,LI Shaoqing,XU Teng. Flatness Prediction based on Elman Network with Artificial Bee Colony Algorithm[J]. Journal of Shenyang University, 2012, 24(3): 38-42 |
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| Authors: | ZHANG Xiuling ZHAO Wenbao LI Shaoqing XU Teng |
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| Affiliation: | 1. Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China 2. National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Qinhuangdao 066004, China) |
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| Abstract: | Because the ability of generalization of routine Elman network is poor, and the need of industry for flatness prediction model with high precision is eager, a flatness prediction model based on Elman network that is trained by artificial bee colony （ABC） algorithm instead of error back propagation algorithm is presented. The number of the hidden node is determined by experiential formula and simulation experiments. The results of simulation show that the model trained by ABC has a more powerful ability of generalization than routine Elman network under the same conditions and the accuracy of flatness prediction is higher. |
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| Keywords: | artificial bee colony algorithm Elman network flatness prediction model the ability ofgeneralization |
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