[1]杨海楠,张代远[].基于样条权函数神经网络的传感器故障诊断[J].计算机技术与发展,2014,24(06):204-207.
 YANG Hai-nan[],ZHANG Dai-yuan[][][].Fault Diagnosis of Sensor Based on Spline Weight Function Neural Network[J].,2014,24(06):204-207.
点击复制

基于样条权函数神经网络的传感器故障诊断()
分享到:

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

卷:
24
期数:
2014年06期
页码:
204-207
栏目:
应用开发研究
出版日期:
2014-06-30

文章信息/Info

Title:
Fault Diagnosis of Sensor Based on Spline Weight Function Neural Network
文章编号:
1673-629X(2014)06-0204-04
作者:
杨海楠1张代远12[3]
1.南京邮电大学 计算机学院;2.江苏省无线传感网高技术研究重点实验室;3.南京邮电大学 计算机技术研究所
Author(s):
YANG Hai-nan[1]ZHANG Dai-yuan[1][2][3]
关键词:
样条权函数神经网络传感器故障诊断
Keywords:
spline weight functionneural networksensorfault diagnosis
分类号:
TP39
文献标志码:
A
摘要:
在技术高速发展的今天,传感器用于各行各业,加之这些年来,家用电器、汽车、信息产业三方面的飞速发展,传感器需求量增大,传感器故障诊断技术变得尤为重要,并且对提高系统的可靠性具有重要意义。利用神经网络对传感器故障进行诊断的方法克服了分析冗余方法需要的系统精确数学模型的问题,并且可以处理非线性数据。文中详细阐述了样条权函数神经网络的结构、原理,在分析传感器主要故障的基础上,提出了样条权函数神经网络的传感器故障诊断方案。Matlab仿真和模拟实验结果表明,样条权函数神经网络可以解决传感器故障检测问题。
Abstract:
With rapid development of technology,the sensors has been used in various industries,coupled with the years,the fast develop-ment of three areas of household appliances,automobiles,information industry,increasing demand of sensor,the sensor fault diagnosis technology has become particularly important, and to improve system reliability is significant. Using neural network to analyze sensor faults overcomes the problem of redundancy methods which require accurate mathematical model of the system,and can handle non-line-ar data. It describes the spline weight function neural network structure, principle, and proposes spline weight function neural network means based on analyzing main sensor fault diagnosis. Matlab simulation and simulation results show that the spline weight function neu-ral network can solve the problem of sensor fault detection.

相似文献/References:

[1]路川 胡欣杰.区域航空市场航线客流量预测研究[J].计算机技术与发展,2010,(04):84.
 LU Chuan,HU Xin-jie.Analysis of Regional Airline Passenger Forecast Title[J].,2010,(06):84.
[2]高峥 陈蜀宇 李国勇.混合入侵检测系统的研究[J].计算机技术与发展,2010,(06):148.
 GAO Zheng,CHEN Shu-yu,LI Guo-yong.Research of a Hybrid Intrusion Detection System[J].,2010,(06):148.
[3]包力伟 周俊.铸锻企业生产质量控制系统的开发[J].计算机技术与发展,2008,(04):174.
 BAO Li-wei,ZHOU Jun.Development of a Manufacture Quality Control System in Casting Company[J].,2008,(06):174.
[4]李志俊 程家兴 金奎 饶玉佳.基于样本期望训练数的BP神经网络改进研究[J].计算机技术与发展,2009,(05):103.
 LI Zhi-jun,CHENG Jia-xing,JIN Kui,et al.BP Algorithm Improvement Based on Sample Expected Training Number[J].,2009,(06):103.
[5]李龙澍 葛瑞峰 王慧萍.基于神经网络的批强化学习在Robocup中的应用[J].计算机技术与发展,2009,(07):98.
 LI Long-shu,GE Rui-feng,WANG Hui-ping.Application of Batch Reinforcement Learning Based on NN to Robocup[J].,2009,(06):98.
[6]贾志先.神经网络在空白试卷识别中的应用[J].计算机技术与发展,2009,(08):208.
 JIA Zhi-xian.Application of Neural Network in Recognization Blank Examination Paper[J].,2009,(06):208.
[7]肖宜龙 路游 亓永刚.基于神经网络的NURBS曲面重建[J].计算机技术与发展,2009,(09):65.
 XIAO Yi-long,LU You,QI Yong-gang.NURBS Surface Reconstruction Based on Neural Network[J].,2009,(06):65.
[8]蔡秋茹 罗烨 柳益君 叶飞跃.企业资信的BP神经网络评估模型研究[J].计算机技术与发展,2009,(10):117.
 CAI Qiu-ru,LUO Ye,LIU Yi-jun,et al.Research on BP Neural Network Model for Corporation Credit Rating[J].,2009,(06):117.
[9]王晓敏 刘希玉 戴芬.BP神经网络预测算法的改进及应用[J].计算机技术与发展,2009,(11):64.
 WANG Xiao-min,LIU Xi-yu,DAI Fen.Improvement and Application of BP Neural Network Forecasting Algorithm[J].,2009,(06):64.
[10]崔海青 刘希玉.基于粒子群算法的RBF网络参数优化算法[J].计算机技术与发展,2009,(12):117.
 CUI Hai-qing,LIU Xi-yu.Parameter Optimization Algorithm of RBF Neural Network Based on PSO Algorithm[J].,2009,(06):117.
[11]王家凯,张代远[].样条权函数神经网络在指纹识别中的应用[J].计算机技术与发展,2014,24(06):170.
 WANG Jia-kai[],ZHANG Dai-yuan[][][].Application of Spline Weight Function Neural Network in Fingerprint Recognition[J].,2014,24(06):170.
[12]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
 HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(06):21.
[13]张代远[][][],王雷雷[]. 一类乘性有理样条权函数神经网络灵敏度分析[J].计算机技术与发展,2016,26(10):50.
 ZHANG Dai-yuan[] [][],WANG Lei-lei[]. Sensitivity Analysis of Neural Network with Rational Spline Weight Functions Using Multiplicative Neurons[J].,2016,26(06):50.

更新日期/Last Update: 1900-01-01