[1]江春冬,武玉维,贾科进,等.基于自学习神经网络的运输环境下产品破损评估模型——以鸡蛋为例[J].河北工业大学学报,2015,(05):1-6.[doi:10.14081/j.cnki.hgdxb.2015.05.001]
 JIANG Chun-dong,WU Yu-wei,JIA Ke-jin,et al.Damage Assessment Model of Products in Transportation Based on Self-learning Neural Network ——Take the Eggs[J].Journal of Hebei University of Technology,2015,(05):1-6.[doi:10.14081/j.cnki.hgdxb.2015.05.001]
点击复制

基于自学习神经网络的运输环境下产品破损评估模型——以鸡蛋为例()
分享到:

《河北工业大学学报》[ISSN:1007-2373/CN:13-1208/T]

卷:
期数:
2015年05期
页码:
1-6
栏目:
电气及自动化工程
出版日期:
2015-09-30

文章信息/Info

Title:
Damage Assessment Model of Products in Transportation Based on Self-learning Neural Network ——Take the Eggs
文章编号:
1007-2373(2015)05-0001-07
作者:
江春冬1武玉维1贾科进2杜太行1
(1 河北工业大学控制科学与工程学院,天津 300130; 2 河北科技大学电气工程学院,河北 石家庄,050018)
Author(s):
JIANG Chun-dong1 WU Yu-wei1 JIA Ke-jin2 DU Tai-hang1
(1 School of Control Science and Engineering, Hebei University of Technology, Tianjin, 300130 , China; 2 School of Electrical Engineering, Hebei University of Science and Technology, Hebei, Shijiazhuang, 050018, China)
关键词:
随机振动运输神经网络评估模型鸡蛋�B
Keywords:
random vibration transport neural network evaluating model egg
分类号:
TP183
DOI:
10.14081/j.cnki.hgdxb.2015.05.001
文献标志码:
A
摘要:
针对运输环境中随机振动引起的产品破损问题,以鸡蛋为例建立了具有自学习功能的基于BP神经网络的产品破损评估模型。重点分析了随机振动下产品的破损机理,研究了随机振动信号频率谱分析方法,利用疲劳累积损伤理论给出了运输环境随机振动下产品破损概率分析理论,为模型的建立提供了充分的理论依据。具体分析了鸡蛋在运输过程中的特点,确定了模型的结构及输入层、隐含层、输出层神经元个数及意义。通过模拟运输环境对200箱鸡蛋进行了振动试验统计的样本数据,对模型进行了训练和测试。结果表明该模型具有较高的评估精度和较好的泛化能力,为研究多因素下产品破损评估模型提供了一定的基础。
Abstract:
According to the problem that product damage caused by random vibration during transportation, a model based on self-learning neural network was established to assess the state of the products that took the eggs as example. Focusing on the analysis of damage mechanism of products under random vibration, studying the method to random vibration signal frequency spectrum and giving the result of product damage probability in transportation with fatigue cumulative damage theory, all above provided sufficient theoretical basis for the model establishment. Specifically, the characteristics of the eggs were analyzed in the process of transportation, determining the model structure, the number and significance of neurons of input layer, hidden layer and output layer were determined. According to the sample data for 200 cases of eggs from the vibration testing by simulating conditions of transport, the model was trained and tested. The results showed that the model had high precision and good generalization ability, and provided a certain basis for the research on the product damage assessment model under multiple factors.

参考文献/References:

[1] 王军,卢立新,王志伟.产品破损评价及防护包装力学研究[J].振动与冲击,2010,29(08):43-45+51+241.
[2] 刘萌,张振富,王美兰,等.不同包装方式对蓝莓物流及货架期质构品质的影响[J].食品工业科技,存2013,34(23):323-327.
[3] 赵岩,张亚辉,林家浩.车辆随机振动功率谱分析的虚拟激励法概述[J].应用数学和力学,2013,34(02):107-117.
[4] To C W S. Nonlinear random vibration: Analytical techniques and applications[M]. CRC Press, 2011.
[5] 段虎明,马颖,石锋,等. 道路路面测量数据的特征参数提取与统计分析[J].振动与冲击,2013,32(01):30-34+42.
[6] 袁洪,张惠兴.平稳信号功率谱密度的估计方法[J].昆明冶金高等专科学校学报,2011,27(03):45-47.
[7] 萨昊亮,李成良,余启明,等.风电叶片疲劳试验振动分析与研究[J].玻璃钢/复合材料,2013,(02):57-59.
[8] 曾祥燕,赵良忠,孙文兵,等.基于PCA和BP神经网络的葡萄酒品质预测模型[J].食品与机械,2014,30(01):40-44.
[9] 周英,尹邦德,任铃,等.基于BP神经网络的电网短期负荷预测模型研究[J].电测与仪表,2011,48(2):68-71.
[10] 王巧华,任奕林,文友先.基于BP神经网络的鸡蛋新鲜度无损检测方法[J].农业机械学报,2006,01:104-106.
[11]窦立阳,刘建周.基于BP人工神经网络的软件质量评估[J].计算机与数字工程,2014,04:644-646.
[12]彭辉,文友先,王巧华,等.基于小波变换和BP神经网络的蛋壳破损检测[J].农业机械学报,2009,02:170-174.
[13]刘晓莉,戎海武.基于MATLAB的BP神经网络算法在多元非线性系统建模中的应用[J].软件导刊,2013,10:66-67.
[14]王玲.基于BP算法的人工神经网络建模研究[J].装备制造技术,2014,01:162-164+169.
[15]Beale M H, Hagan M T, Demuth H B. Neural Network Toolbox 7[J]. User’s Guide, MathWorks, 2010.
[16]Xian W, Tian W, Shunbin S, et al. Application of the prediction of monthly domestic water consumption based on MATLAB BP neural network[J]. Water Technology, 2011, 6: 011.
[17]Moula N, Ait-Kaki A, Leroy P, et al. Quality assessment of marketed eggs in bassekabylie (Algeria)[J]. Revista Brasileira de Ciência Avícola, 2013, 15(4): 395-399.
[18]Guo Z, Wu J, Lu H, et al. A case study on a hybrid wind speed forecasting method using BP neural network[J]. Knowledge-based systems, 2011, 24(7): 1048-1056.

相似文献/References:

[1]高炳军,王滨,翟兰惠,等. 吊带支撑低温储罐运输中随机振动分析[J].河北工业大学学报,2018,(01):48.
 [J].Journal of Hebei University of Technology,2018,(05):48.

备注/Memo

备注/Memo:
收稿日期:2015-09-06 基金项目:河北省自然科学基金(F2014202264);国家自然科学基金(51207043) 作者简介:江春冬(1974-),女(汉族),讲师,博士.
更新日期/Last Update: 2017-01-18