[1]孟令启,王海龙,马金亮,等.基于RBF神经网络的金属应力状态系数模型[J].郑州大学学报(工学版),2007,28(01):1-5.[doi:10.3969/j.issn.1671-6833.2007.01.001]
　Meng Lingqi,Wang Hailong,Ma Jinliang,et al.Metal stress state coefficient model based on RBF neural network[J].Journal of Zhengzhou University (Engineering Science),2007,28(01):1-5.[doi:10.3969/j.issn.1671-6833.2007.01.001]

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基于RBF神经网络的金属应力状态系数模型()

分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:

28卷

期数:

2007年01期

页码:

1-5

栏目:

出版日期:

1900-01-01

文章信息/Info

Title:

Metal stress state coefficient model based on RBF neural network

作者:

孟令启; 王海龙; 马金亮; 等.

郑州大学,机械工程学院,河南,郑州,450001, 郑州大学,机械工程学院,河南,郑州,450001, 郑州大学,机械工程学院,河南,郑州,450001, 郑州大学,机械工程学院,河南,郑州,450001

Author(s):

Meng Lingqi; Wang Hailong; Ma Jinliang; etc

关键词:

应力状态影响系数;  RBF神经网络;  模型

Keywords:

Stress state influence coefficient;  RBF neural networks;  Model

DOI:

10.3969/j.issn.1671-6833.2007.01.001

文献标志码:

A

摘要:

以4 200 mm轧机轧制71块钢板的实测数据为基础,利用Matlab人工神经网络工具箱,建立了轧制变形区的应力状态系数的RBF神经网络预测模型.通过分析应力状态系数的影响因素,结合传统的数学模型,确立了网络的输入层参数,并对函数newrb()中宽度系数spread的试验调整,确定了最佳的网络结构形式,提高了模型的预测精度以及网络的泛化能力.测试结果表明,RBF网络模型具有很好的推广能力.与传统的BP神经网络模型相比较,结果表明,RBF网络具有更高的精度和更好的泛化能力.

Abstract:

Based on the measured data of 4 steel plates rolled by 200 71 mm rolling mill, the RBF neural network prediction model of stress state coefficient in the rolling deformation zone was established by using the Matlab artificial neural network toolbox. By analyzing the influencing factors of the stress state coefficient and combining with the traditional mathematical model, the input layer parameters of the network are established, and the experimental adjustment of the width coefficient spread in the function newrb() determines the optimal network structure form, which improves the prediction accuracy of the model and the generalization ability of the network. The test results show that the RBF network model has good generalization ability. Compared with the traditional BP neural network model, the results show that the RBF network has higher accuracy and better generalization ability.

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