[1]吕兵,王华珍,潘孝铭.神经网络的压力容器评估系统设计[J].华侨大学学报(自然科学版),2014,35(5):528-532.[doi:10.11830/ISSN.1000-5013.2014.05.0528]
　LYU Bing,WANG Hua-zhen,PAN Xiao-ming.Design of Pressure Vessel Evaluation System Based on Artificial Neural Networks[J].Journal of Huaqiao University(Natural Science),2014,35(5):528-532.[doi:10.11830/ISSN.1000-5013.2014.05.0528]
点击复制

神经网络的压力容器评估系统设计()
分享到：

《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第35卷
期数:
2014年第5期
页码:
528-532
栏目:
出版日期:
2014-09-20

文章信息/Info

Title:
Design of Pressure Vessel Evaluation System Based on Artificial Neural Networks
文章编号:
1000-5013(2014)05-0528-05
作者:
吕兵;  王华珍;  潘孝铭
华侨大学 计算机科学与技术学院, 福建 厦门 361021
Author(s):
LYU Bing;  WANG Hua-zhen;  PAN Xiao-ming
College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
关键词:
压力容器;  评估;  人工神经网络;  混合编程
Keywords:
pressure vessel;  evaluation;  artificial neural networks;  mixed programming
分类号:
TP183
DOI:
10.11830/ISSN.1000-5013.2014.05.0528
文献标志码:
A
摘要:
为解决长周期压力容器设备安全评估的低效率、低可靠性和不能人机交互等问题,利用开源的R语言设计复杂的神经网络识别算法,并通过C#.NET设计出一套界面友好的压力容器评估系统.实验结果表明:创建的压力容器评估系统嵌入BP神经网络算法,能精确刻画压力容器参数与状态之间的复杂非线性关系,评估准确率高;同时,软件系统实现了评估过程的交互性和自动化,具有良好的用户体验和很强的实践性.
Abstract:
To address the problems of low efficiency,low reliability,without human-computer interaction in the safety evaluation of the long-periodic pressure vessel, an intelligent evaluation system based on artificial neural network algorithm is established. In which the open source R language is used to design the complex neural network intelligent algorithm and a user-friendly operating system is developed through C#.NET technology. The experimental results show that the pressure vessel evaluation system embedded BP neural network algorithm can precisely figure out the complex nonlinear relationship between the parameters and the state of pressure vessel by significantly high accuracy. Meanwhile, the software system promotes the interactivity and automation of the evaluation process, which gives good user experience and strong practicality.

参考文献/References:

[1] 韩毅,淡勇,李小勇,等.含缺陷压力容器安全评定的发展历程与趋势[J].石油化工设备技术,2012,33(4):47-50,71.
[2] 龙伟,杜仕冲,余进.基于含缺陷在役压力容器的模糊评定[J].四川大学学报:工程科学版,2007,39(1):166-170.
[3] 戴树和.化工装置在线检查诊断中的非精确性推理[J].化工学报,1994,45(2):141-146.
[4] 陈国华.含缺陷压力容器失效概率分析方法初步研究[J].化工机械,1996,23(4):42-45,63.
[5] 俞树荣,李尔国,贾立.基于人工神经网络的压力容器初级评定方法[J].化工机械,1999,26(3):54-56,64.
[6] 俞树荣,李尔国,贾立.人工神经网络与失效评定图在压力容器安全评定中的应用[J].化工机械,1999,26(5):280-283,301.
[7] HAYKIN S.Neural networks and learning machines[M].New York:Prentice Hall,2009:122-154.
[8] GUYON I,WESTON J,BARNHILL S,et al.Gene selection for cancer classification using support vector machines[J].Machine Learning,2002,46(1/2/3):389-422.
[9] DIETTERICH T G.Ensemble methods in machine learning[M].Springer:Berlin Heidelberg,2000:1-15.
[10] 马锐.人工神经网络原理[M].北京:机械工业出版社,2010:45-49.
[11] M?LLER M F.A scaled conjugate gradient algorithm for fast supervised learning[J].Neural Networks,1993,6(4):525-533.
[12] 赵毅,史权,赵锁奇,等.R语言与.NET混合编程在重质油数据管理分析中的应用[J].计算机与应用化学,2012,29(4):491-494.

相似文献/References:

[1]吕兵,王华珍,潘孝铭.神经网络的压力容器评估系统设计[J].华侨大学学报(自然科学版),2015,36(预先出版):0.
　LYU Bing,WANG Hua-zhen,PAN Xiao-ming.Design of Pressure Vessel Evaluation System Based on Artificial Neural Networks[J].Journal of Huaqiao University(Natural Science),2015,36(5):0.

备注/Memo

备注/Memo:
收稿日期: 2013-09-26
通信作者: 王华珍(1975-),女,讲师,主要从事机器学习、模式识别的研究.E-mail:wanghuazhen@hqu.edu.cn.
基金项目: 福建省自然科学基金资助项目(2012J01274); 华侨大学高层次人才科研项目(09BS515)
更新日期/Last Update: 2014-09-20