[1]任少君,司风琪,李欢欢,等.基于混合型鲁棒输入训练神经网络的非线性数据校正方法及其应用[J].东南大学学报(自然科学版),2013,43(2):322-327.[doi:10.3969/j.issn.1001-0505.2013.02.018]
 Ren Shaojun,Si Fengqi,Li Huanhuan,et al.Nonlinear data correction method and its application based on hybrid robust input-training neural network[J].Journal of Southeast University (Natural Science Edition),2013,43(2):322-327.[doi:10.3969/j.issn.1001-0505.2013.02.018]
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基于混合型鲁棒输入训练神经网络的非线性数据校正方法及其应用()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
43
期数:
2013年第2期
页码:
322-327
栏目:
自动化
出版日期:
2013-03-20

文章信息/Info

Title:
Nonlinear data correction method and its application based on hybrid robust input-training neural network
作者:
任少君司风琪李欢欢徐治皋
东南大学能源热转换及其过程测控教育部重点实验室, 南京210096
Author(s):
Ren Shaojun Si Fengqi Li Huanhuan Xu Zhigao
Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast University, Nanjing 210096, China
关键词:
混合型鲁棒输入训练神经网络 故障诊断 机理约束 罚函数 数据校正
Keywords:
hybrid robust input-training neural network fault diagnosis mechanism constraints penalty function data correction
分类号:
TP206.3
DOI:
10.3969/j.issn.1001-0505.2013.02.018
摘要:
提出了一种基于混合型鲁棒输入训练神经网络的非线性数据校正模型,在基于过程数据的神经网络模型中引入了反映过程机理的约束方程.根据所提模型的网络结构,采用罚函数法将约束方程加入到网络训练目标函数中,并采用BP算法推导出该网络的学习方法,进而给出了基于该方法的数据校正流程.分别以一个五维非线性系统和某1 000 MW机组1#高加测点为对象进行算例分析,结果表明:所提出的模型能正确检验出测量数据中的不良值,具有良好的鲁棒性;在完成数据校正的同时还能保证重构数据满足相应的系统机理约束条件;在多测点同时发生故障时,也能保证数据校正的准确性和可靠性.
Abstract:
A nonlinear data correction model, hybrid robust modified input-training neural network model, is presented by integrating process mechanism constraint equations into the neutral network model. In the network structure of proposed model, constraint equations are included in the objective function of network training by penalty function method. The network learning method is derived based on BP(back propagation)algorithm, and the algorithm steps are also given in this paper. The case study is conducted to detect and validate some data sets measured from and 1# high pressure heater in a 1 000 MW unit and a five-dimension nonlinear system, and the result indicates the validity and robustness of the proposed model,which can ensure the accuracy and reliability of data correction in case of multi-point fault under system mechanism constraints.

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备注/Memo

备注/Memo:
作者简介: 任少君(1989—),男,博士生;司风琪(联系人),男,博士,教授,博士生导师,fqsi@seu.edu.cn.
基金项目: 国家自然科学基金资助项目(51176030).
引文格式: 任少君,司风琪,李欢欢,等.基于混合型鲁棒输入训练神经网络的非线性数据校正方法及其应用[J].东南大学学报:自然科学版,2013,43(2):322-327. [doi:10.3969/j.issn.1001-0505.2013.02.018]
更新日期/Last Update: 2013-03-20