|本期目录/Table of Contents|

[1]程诚,任佳.基于自适应卷积核的改进CNN数值型数据分类算法[J].浙江理工大学学报,2019,41-42(自科五):657-664.
 CHENG Cheng,REN Jia.Improved CNN classification algorithm based on adaptive convolution kernel for numerical data[J].Journal of Zhejiang Sci-Tech University,2019,41-42(自科五):657-664.
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基于自适应卷积核的改进CNN数值型数据分类算法()
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浙江理工大学学报[ISSN:1673-3851/CN:33-1338/TS]

卷:
第41-42卷
期数:
2019年自科五期
页码:
657-664
栏目:
出版日期:
2019-09-18

文章信息/Info

Title:
Improved CNN classification algorithm based on adaptive convolution kernel for numerical data
文章编号:
1673-3851 (2019) 09-0657-08
作者:
程诚任佳
浙江理工大学机械与自动控制学院,杭州310018
Author(s):
CHENG ChengREN Jia
Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China
关键词:
卷积神经网络数值型数据自适应卷积核网格搜索
分类号:
TP181
文献标志码:
A
摘要:
针对卷积神经网络(Convolutional neural network,CNN)模型在对工业数值型数据分类方面存在特征使用不充分、模型分类性能不佳等问题,提出了一种基于自适应卷积核的改进CNN(Improved CNN based on adaptive convolution kernel, ACKICNN)算法。该算法为了增加特征的重复使用率,构建了一种多尺度卷积核的模型结构,通过融合处理卷积核提取的不同特征来实现,增强了模型的适应能力;为了进一步提升该算法的性能,利用网格搜索算法自适应选取CNN中最优的卷积核大小,使得模型能够提取出最优的特征。采用TE过程的故障数据对其进行测试,并与支持向量机、极限学习机、最近邻等典型的数据驱动方法进行对比,测试结果表明,该算法能有效提升各类故障的分类精度。

参考文献/References:

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[8] Jia F, Lei Y, Lu N, et al. Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization[J]. Mechanical Systems & Signal Processing, 2018, 110:349-367.
[9] 林颖, 郭志红, 陈玉峰. 基于卷积递归网络的电流互感器红外故障图像诊断[J]. 电力系统保护与控制, 2015, 43(16):87-94.
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备注/Memo

备注/Memo:
收稿日期:2018-12-17
网络出版日期: 2019-02-28
基金项目:国家自然科学基金项目(61203177);浙江省自然科学基金项目(LY17F030024)
作者简介:程诚(1993-),男,安徽阜阳人,硕士研究生,主要从事软测量建模方面的研究
通信作者:任佳,E-mail:jren@zstu.edu.cn
更新日期/Last Update: 2019-09-18