[1]刘威,刘尚,周璇.BP神经网络子批量学习方法研究[J].智能系统学报编辑部,2016,11(2):226-232.[doi:10.11992/tis.201509015]
 LIU Wei,LIU Shang,ZHOU Xuan.Subbatch learning method for BP neural networks[J].CAAI Transactions on Intelligent Systems,2016,11(2):226-232.[doi:10.11992/tis.201509015]
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BP神经网络子批量学习方法研究(/HTML)
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《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
第11卷
期数:
2016年2期
页码:
226-232
栏目:
出版日期:
2016-04-25

文章信息/Info

Title:
Subbatch learning method for BP neural networks
作者:
刘威 刘尚 周璇
辽宁工程技术大学 理学院, 辽宁 阜新 123000
Author(s):
LIU Wei LIU Shang ZHOU Xuan
College of Science, Liaoning Technical University, Fuxin 123000, China
关键词:
子批量学习神经网络BP算法批量尺寸训练方法评估分类
Keywords:
subbatch learningneural networkbackpropagation algorithmsbatch sizetraining methods and evaluationclassification
分类号:
TP301.6
DOI:
10.11992/tis.201509015
摘要:
针对浅层神经网络全批量学习收敛缓慢和单批量学习易受随机扰动的问题,借鉴深度神经网基于子批量的训练方法,提出了针对浅层神经网络的子批量学习方法和子批量学习参数优化配置方法。数值实验结果表明:浅层神经网络子批量学习方法是一种快速稳定的收敛算法,算法中批量和学习率等参数配置对于网络的收敛性、收敛时间和泛化能力有着重要的影响,学习参数经优化后可大幅缩短网络收敛迭代次数和训练时间,并提高网络分类准确率。
Abstract:
When solving problems in shallow neural networks, the full-batch learning method converges slowly and the single-batch learning method fluctuates easily. By referring to the subbatch training method for deep neural networks, this paper proposes the subbatch learning method and the subbatch learning parameter optimization and allocation method for shallow neural networks. Experimental comparisons indicate that subbatch learning in shallow neural networks converges quickly and stably. The batch size and learning rate have significant impacts on the net convergence, convergence time, and generation ability. Selecting the optimal parameters can dramatically shorten the iteration time for convergence and the training time as well as improve the classification accuracy.

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备注/Memo

备注/Memo:
收稿日期:2015-9-7;改回日期:。
基金项目:国家自然科学基金项目(51304114,71371091).
作者简介:刘威,男,1977年生,副教授,博士,中国计算机学会会员,主要研究方向为模式识别、时间序列数据挖掘、矿业系统工程;刘尚,男,1988年生,硕士研究生,主要研究方向为模式识别、人工智能、计算机视觉;周璇,女,1992年生,硕士研究生,主要研究方向为模式识别、矿业系统工程。
通讯作者:刘尚.E-mail:whiteinblue@126.com.
更新日期/Last Update: 1900-01-01