| 基于径向基函数网络的微合金钢流变应力的预测 |
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| 引用本文： | 刘芳,李海涛,邓樱,牛济泰.基于径向基函数网络的微合金钢流变应力的预测[J].塑性工程学报,2001,8(4):9-12. |
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| 作者姓名： | 刘芳 李海涛 邓樱 牛济泰 |
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| 作者单位： | 哈尔滨工业大学, |
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| 基金项目： | 本项目由东北大学轧制技术与连轧自动化国家重点实验室资助. |
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| 摘 要： | 本文建立了预报微合金钢热变形中流变应力的径向基函数网络模型。与通常的BP函数网络模型进行了对比 ,并与实测结果进行校核。结果表明 ,对于本文研究的问题 ,径向基函数网络避免了BP网络的局部极小及收敛速度慢等缺点 ,在精度、训练速度等方面优于BP网络
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| 关 键 词： | 径向基 人工神经网络 流变应力 微合金钢 |
PREDICTION OF FLOW STRESS OF MICROALLOYED STEEL USING A RADIAL BASIS FUNCTION NETWORK |
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| LIU Fang,LI Hai\|tao,DENG Ying,NIU Ji\|tai.PREDICTION OF FLOW STRESS OF MICROALLOYED STEEL USING A RADIAL BASIS FUNCTION NETWORK[J].Journal of Plasticity Engineering,2001,8(4):9-12. |
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| Authors: | LIU Fang LI Hai\|tao DENG Ying NIU Ji\|tai |
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| Affiliation: | Harbin Institute of Technology Harbin 150001 |
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| Abstract: | A radial basis function neural network model on flow stress of micro\|alloyed steel under hot deformation is established in this paper.The stress-strain curves of micro\|alloyed steel under different temperatures and strain rates are tested by using physical simulating method.The prediction model based on radial basis function network is trained through experimental data and verified by additional data successfully.Another network model based on backpropagation network is also trained for comparison.The results show that for the problem studied in this paper,the radial basis function network is much better than backpropagation network in accuracy and speed of straining. |
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| Keywords: | radial basis function artificial neural network flow stress micro\|alloyed steel |
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