[1]王嵘冰,徐红艳,李 波,等.BP 神经网络隐含层节点数确定方法研究[J].计算机技术与发展,2018,28(04):31-35.[doi:10.3969/ j. issn.1673-629X.2018.04.007]
 WANG Rong-bing,XU Hong-yan,LI Bo,et al.Research on Method of Determining Hidden Layer Nodes in BP Neural Network[J].,2018,28(04):31-35.[doi:10.3969/ j. issn.1673-629X.2018.04.007]
点击复制

BP 神经网络隐含层节点数确定方法研究()
分享到:

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年04期
页码:
31-35
栏目:
智能、算法、系统工程
出版日期:
2018-04-10

文章信息/Info

Title:
Research on Method of Determining Hidden Layer Nodes in BP Neural Network
文章编号:
1673-629X(2018)04-0031-05
作者:
王嵘冰徐红艳李 波冯 勇
辽宁大学 信息学院,辽宁 沈阳 110036
Author(s):
WANG Rong-bingXU Hong-yanLI BoFENG Yong
School of Information,Liaoning University,Shenyang 110036,China
关键词:
BP 神经网络隐含层节点三分法最优解
Keywords:
BP neural networkhidden layer nodesternary algorithmoptimal solution
分类号:
TP301.6
DOI:
10.3969/ j. issn.1673-629X.2018.04.007
文献标志码:
A
摘要:
BP 神经网络的众多参数中,隐含层节点数是其中一个非常重要的参数,它的设置对 BP 神经网络的性能影响很大,而且是导致“过拟合”现象的直接原因。 目前理论上还不存在一种科学普遍的用于确定隐含层节点数的方法,应用时只是凭借设计者以往的经验以及借助多次实验进行确定,因此无法高效地获得隐含层节点数。 针对 BP 神经网络隐含层节点数的确定问题,提出一种“三分法”算法,用于快速确定 BP 神经网络的隐含层节点数的最优解。 在 Wine-data 数据集上,通过 Matlab 仿真实验验证了“三分法”算法比传统方法获取隐含层节点数的效率提高了 1. 8 倍,是一种行之有效的
方法。
Abstract:
In the many parameters of BP neural networks,the number of nodes in the hidden layer is a very important one,which has a great influence on the performance of BP neural networks,and it is the immediate cause of the phenomenon of over-fitting. At present,there is no scientific and universal method to determine the number of nodes in the hidden layer. In application it is determined just by the designer,s experience and the help of many experiments,therefore the number of hidden layer nodes can,t be obtained efficiently. Aiming at the problem,we propose a ternary algorithm to rapidly determine the optimal solution of the number of hidden layer nodes in BP neural networks. In the data set of Wine-data,it is proved that the efficiency of the proposed method which is effective is 1.8 times higher than the traditional method through Matlab simulation.

相似文献/References:

[1]张义超 卢英 李炜.RBF网络隐含层节点的优化[J].计算机技术与发展,2009,(01):103.
 ZHANG Yi-chao,LU Ying,LI Wei.RBF Network of Hidden Layer Nodes Optimization[J].,2009,(04):103.
[2]浦佳祺,陈德旺.基于最小二乘法和BP神经网络的TOA 定位算法[J].计算机技术与发展,2018,28(05):5.[doi:10.3969/j.issn.1673-629X.2018.05.002]
 PU Jia-qi,CHEN De-wang.A TOA Positioning Algorithm Based on Least Square Method and BP Neural Network[J].,2018,28(04):5.[doi:10.3969/j.issn.1673-629X.2018.05.002]
[3]李春生,李霄野,张可佳.基于遗传算法改进的 BP 神经网络房价预测分析[J].计算机技术与发展,2018,28(08):144.[doi:10.3969/ j. issn.1673-629X.2018.08.030]
 LI Chun-sheng,LI Xiao-ye,ZHANG Ke-jia.Price Forecasting Analysis of BP Neural Network Based on Improved Genetic Algorithm[J].,2018,28(04):144.[doi:10.3969/ j. issn.1673-629X.2018.08.030]
[4]辛月振,孙贝贝,夏盛瑜.数据挖掘方法在生物实验数据上的应用[J].计算机技术与发展,2018,28(09):143.[doi:10.3969/ j. issn.1673-629X.2018.09.029]
 XIN Yue-zhen,SUN Bei-bei,XIA Sheng-yu.Application of Data Mining Method in Biological Experiment Data[J].,2018,28(04):143.[doi:10.3969/ j. issn.1673-629X.2018.09.029]
[5]丁红卫,王文果,万 良,等.基于 BP 神经网络的电网物资需求预测研究[J].计算机技术与发展,2019,29(06):138.[doi:10. 3969 / j. issn. 1673-629X. 2019. 06. 029]
 DING Hong-wei,WANG Wen-guo,WAN Liang,et al.Research on Material Demand Prediction of Power Network Based on BP Neural Network[J].,2019,29(04):138.[doi:10. 3969 / j. issn. 1673-629X. 2019. 06. 029]
[6]姬晓飞,石宇辰.多分类器融合的光学遥感图像目标识别算法[J].计算机技术与发展,2019,29(11):52.[doi:10. 3969 / j. issn. 1673-629X. 2019. 11. 011]
 JI Xiao-fei,SHI Yu-chen.Optical Remote Sensing Image Object Recognition Based on Multiple Classifications Fusion[J].,2019,29(04):52.[doi:10. 3969 / j. issn. 1673-629X. 2019. 11. 011]
[7]邓晓政,叶 冰.免疫 BP 网络的机载嵌入式训练系统效能评估[J].计算机技术与发展,2019,29(12):173.[doi:10. 3969 / j. issn. 1673-629X. 2019. 12. 031]
 DENG Xiao-zheng,YE Bing.Effectiveness Evaluation of Airborne Embedded Training System of Immune BP Networks[J].,2019,29(04):173.[doi:10. 3969 / j. issn. 1673-629X. 2019. 12. 031]
[8]李佩钰.一种基于小波和神经网络的短时交通流量预测[J].计算机技术与发展,2020,30(01):135.[doi:10. 3969 / j. issn. 1673-629X. 2020. 01. 024]
 LI Pei-yu.Short-term Traffic Flow Prediction Based on Wavelet and Neural Network[J].,2020,30(04):135.[doi:10. 3969 / j. issn. 1673-629X. 2020. 01. 024]
[9]宋 波,辛文贤,冯云霞.基于 BP 神经网络的临床路径优化[J].计算机技术与发展,2020,30(04):156.[doi:10. 3969 / j. issn. 1673-629X. 2020. 04. 030]
 SONG Bo,XIN Wen-xian,FENG Yun-xia.Clinical Path Optimization Based on BP Neural Network[J].,2020,30(04):156.[doi:10. 3969 / j. issn. 1673-629X. 2020. 04. 030]
[10]余肖生,宋 锦,任明霞,等.基于伪度量的案例推理改进算法[J].计算机技术与发展,2020,30(10):69.[doi:10. 3969 / j. issn. 1673-629X. 2020. 10. 013]
 YU Xiao-sheng,SONG Jin,REN Ming-xia,et al.Improved Case Reasoning Algorithm Based on Pseudo-Metric[J].,2020,30(04):69.[doi:10. 3969 / j. issn. 1673-629X. 2020. 10. 013]

更新日期/Last Update: 2018-06-05