[1]王少波,柴艳丽,梁醒培..神经网络学习样本点的选取方法比较[J].郑州大学学报(工学版),2003,24(01):63-65,69.[doi:10.3969/j.issn.1671-6833.2003.01.014]
 Wang Shaobo,Chai Yanli,Liang Xingpei.Comparison of the selection methods of neural network learning sample points[J].Journal of Zhengzhou University (Engineering Science),2003,24(01):63-65,69.[doi:10.3969/j.issn.1671-6833.2003.01.014]
点击复制

神经网络学习样本点的选取方法比较()
分享到:

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

卷:
24卷
期数:
2003年01期
页码:
63-65,69
栏目:
出版日期:
1900-01-01

文章信息/Info

Title:
Comparison of the selection methods of neural network learning sample points
作者:
王少波柴艳丽梁醒培.
郑州机械研究所,河南,郑州,450052, 郑州机械研究所,河南,郑州,450052, 郑州机械研究所,河南,郑州,450052
Author(s):
Wang Shaobo; Chai Yanli; Liang Xingpei
关键词:
神经网络 正交设计 均匀设计
Keywords:
DOI:
10.3969/j.issn.1671-6833.2003.01.014
文献标志码:
A
摘要:
为了比较训练人工神经网络的所需样本点的选取,分别采用随机遍历法、正交设计法和均匀设计方法产生样本点,用于训练神经网络.分析结果表明,在样本点个数相同情况下,均匀设计法的代表性最好,正交设计法次之,而随机遍历法较差.随机遍历法随着样本点个数的增多,同样可以提高其代表性.当函数随变量在区间内变化较小(因素水平可以取的较少)时,正交设计法也不失为一个好的选择.均匀设计法在多变量,且每个变量需要选取较多水平数的情况下,更能体现它的优越性.
Abstract:
In order to compare the selection of sample points required for training artificial neural networks, the random traversal method, orthogonal design method and uniform design method are used to generate sample points for training neural networks. The analysis results show that under the same number of sample points, the uniform design method is the best representative, followed by the orthogonal design method, and the random traversal method is poor. The random traversal method can also improve its representativeness with the increase of the number of sample points. When the function varies less with the variable in the interval (less factor level can be taken), the orthogonal design method is also a good choice. The uniform design method can better reflect its superiority in the case of multiple variables and each variable needs to select a larger number of levels.

相似文献/References:

[1]蒋建东,张豪杰,王静.基于HHT的电力负荷组合预测应用[J].郑州大学学报(工学版),2015,36(04):1.[doi:10.3969/ j. issn.1671 - 6833.2015.04.001]
 JIANG Jian-dong,ZHANG Hao-jie,WANG Jing.Research and Application of HHT-Based Power Load Combination Forecasting[J].Journal of Zhengzhou University (Engineering Science),2015,36(01):1.[doi:10.3969/ j. issn.1671 - 6833.2015.04.001]
[2]邓万宇,李力,牛慧娟.基于Spark的并行极速神经网络[J].郑州大学学报(工学版),2016,37(05):47.[doi:10.3969/ j.issn.1671 -6833.2016.05.010]
 Deng Wanyu,Li Li,Niu Huijuan.Sparked-based Parallel Extreme Learning Machine[J].Journal of Zhengzhou University (Engineering Science),2016,37(01):47.[doi:10.3969/ j.issn.1671 -6833.2016.05.010]
[3]肖斌,张恒宾,刘宏伟.改进PSO-BPNN算法在管道腐蚀预测中的应用[J].郑州大学学报(工学版),2022,43(01):27.[doi:10.13705/j.issn.1671-6833.2022.01.008]
 XIAO Bin,ZHANG Hengbin,LIU Hongwei.Application of Improved PSO-BPNN Algorithm in Corroded Pipelines Prediction[J].Journal of Zhengzhou University (Engineering Science),2022,43(01):27.[doi:10.13705/j.issn.1671-6833.2022.01.008]
[4]任锐,谢永利..海底隧道竖井口污染物扩散的数值分析及应用[J].郑州大学学报(工学版),2010,31(05):90.[doi:10.3969/j.issn.1671-6833.2010.05.022]
[5]杨华芬,杨有,尚晋..一种改进的进化神经网络优化设计方法[J].郑州大学学报(工学版),2010,31(05):116.[doi:10.3969/j.issn.1671-6833.2010.05.028]
[6]张宝森,王忠福,田治宗,等.黄河下游预应力管桩丁坝结构优化设计研究[J].郑州大学学报(工学版),2012,33(05):96.[doi:10.3969/j.issn.1671-6833.2012.05.021]
[7]周洪煜,陈晓煜,徐春霞..预测控制在中央空调净化系统中的应用[J].郑州大学学报(工学版),2008,29(03):73.[doi:10.3969/j.issn.1671-6833.2008.03.019]
 ZHOU Hongyu,CHEN Xiaoyu,Xu Chunxia.Application of predictive control in central air conditioning purification system[J].Journal of Zhengzhou University (Engineering Science),2008,29(01):73.[doi:10.3969/j.issn.1671-6833.2008.03.019]
[8]郭克希,谭佩莲,唐进元..基于人工神经网络的螺旋锥齿轮磨削加工表面粗糙度预测[J].郑州大学学报(工学版),2009,30(03):65.
 GUO Kexi,TAN Peilian,TANG Jinyuan.Surface Roughness Forecasting of Spiral Bevel Gear Based on Artificial Neural Network[J].Journal of Zhengzhou University (Engineering Science),2009,30(01):65.
[9]薛鹏飞,毛达岭,刘立新..改性砂浆砌体受剪性能的试验研究[J].郑州大学学报(工学版),2006,27(01):48.[doi:10.3969/j.issn.1671-6833.2006.01.012]
[10]刘伟,刘赞,王玲玲..神经网络与结构编码法预测直馏汽油色谱保留指数[J].郑州大学学报(工学版),2004,25(03):26.[doi:10.3969/j.issn.1671-6833.2004.03.007]
 LIU Wei,LIU Zan,Wang Lingling.Neural network and structural coding method to predict the chromatographic retention index of straight-run gasoline[J].Journal of Zhengzhou University (Engineering Science),2004,25(01):26.[doi:10.3969/j.issn.1671-6833.2004.03.007]

更新日期/Last Update: 1900-01-01