| 基于高维输入小波神经网络的热连轧机产品质量模型 |
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| 引用本文： | 李换琴,万百五. 基于高维输入小波神经网络的热连轧机产品质量模型[J]. 工程数学学报, 2006, 23(4): 614-618 |
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| 作者姓名： | 李换琴 万百五 |
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| 作者单位： | 1. 西安交通大学理学院,西安,710049
2. 西安交通大学系统工程研究所,西安,710049 |
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| 基金项目： | The National Natural Science Foundation of China (60574060). |
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| 摘 要： | 小波神经网络是一种以小波函数为激励函数的神经网络。现有的小波神经网络局限于低维,本文提出一种适合高维输入的小波神经网络建模方法,对几种小波函数与学习算法进行了比较实验,成功地解决了32维输入的大型多辊热连轧机钢板材质量建模问题。
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| 关 键 词： | 小波神经网络 质量模型 高维输入 热连轧机 |
| 文章编号： | 1005-3085(2006)04-0614-05 |
| 收稿时间： | 2004-09-27 |
| 修稿时间： | 2004-09-27 |
Using High Dimension Wavelet Neural Network Model Product Quality for Hot Rolling Mill |
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| LI Huan-qin,WAN Bai-wu. Using High Dimension Wavelet Neural Network Model Product Quality for Hot Rolling Mill[J]. Chinese Journal of Engineering Mathematics, 2006, 23(4): 614-618 |
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| Authors: | LI Huan-qin WAN Bai-wu |
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| Abstract: | Wavelet networks now available are usually limited to problems of small dimension input. In this paper, a wavelet-based neural network (WNN) was introduced for handling high dimension input problems. Several different wavelet functions and different algorithms have been tested and compared. Simulation results demonstrate that the B-spline wavelet function and the Levenberg-Marquardt algorithm are effective. The WNN is taken as the production quality model of large-scale hot steel rolling mill and a 32-input modeling problem is successfully solved. |
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| Keywords: | wavelet neural network quality model high-dimension input hot rolling mill |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
