| 基于神经元网络的钻杆热处理温度信号校正 |
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| 引用本文： | 林惠雪,孙青林,陈增强,袁著祉.基于神经元网络的钻杆热处理温度信号校正[J].上海海事大学学报,2001,22(3):210-213. |
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| 作者姓名： | 林惠雪 孙青林 陈增强 袁著祉 |
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| 作者单位： | 林惠雪（南开大学天津 300071）
孙青林（南开大学天津 300071）
陈增强（南开大学天津 300071）
袁著祉（南开大学天津 300071） |
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| 基金项目： | 国家863/CIMS主题基金(863-511-945-010)及教育部骨干教师计划资助. |
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| 摘 要： | 神经网络是非线性系统建模的重要方法.反向传播(BP)算法常常用于神经网络的权值训练中,但是BP算法收敛慢.为此,将非线性最小二乘法用于前馈神经网络的权值学习.采用这一建模方法对石油钻杆在热处理过程中的温度测量偏差进行校正.研究结果表明,该方法具有很快的收敛速度和很好的拟合精度,适用于工业过程中测量信号的在线校正.
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| 关 键 词： | 神经网络 非线性系统建模 最小二乘法 钻杆热处理 温度测量 |
| 修稿时间： | 2001年2月19日 |
Neural-Network-Based Temperature Signals Tuning for Pip e Heating Process |
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| Lin Huixue,Sun Qinglin,Chen Zengqiang,Yuan Zhuzhi.Neural-Network-Based Temperature Signals Tuning for Pip e Heating Process[J].Journal of Shanghai Maritime University,2001,22(3):210-213. |
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| Authors: | Lin Huixue Sun Qinglin Chen Zengqiang Yuan Zhuzhi |
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| Abstract: | Neural Networks is an important method in nonlinear system modeling. Back propagation (BP) algorithm is often used for the weights training of neural network, but the convergence speed of BP algorithm is slow. In this paper, nonlinear least squares method is used for the weights training of feedforward neural networks. Then the modeling method is applied on the tuning of temperature measuring error during the processes of oil pipes are heated. The research result shows that the new algorithm has fast convergence speed and good precision. The method is suitable for the on line tuning of a measured signal on industrial plant. |
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| Keywords: | neural network nonlinear system modeling least squares pipe heating process temperature measurement |
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