[1]刘益剑,狄利明.基于贝叶斯推理模型的时变非线性系统在线输出监测[J].南京师范大学学报(工程技术版),2012,12(02):007-10.
 Liu Yijian,Di Liming.On-line Output Monitoring of Time-Variant Nonlinear System Based on Bayesian Inferring Model[J].Journal of Nanjing Normal University(Engineering and Technology),2012,12(02):007-10.
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基于贝叶斯推理模型的时变非线性系统在线输出监测
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南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
12卷
期数:
2012年02期
页码:
007-10
栏目:
出版日期:
2012-06-20

文章信息/Info

Title:
On-line Output Monitoring of Time-Variant Nonlinear System Based on Bayesian Inferring Model
作者:
刘益剑;狄利明;
南京师范大学电气与自动化工程学院,江苏南京210042
Author(s):
Liu YijianDi Liming
School of Electronic and Automation Engineering,Nanjing Normal University,Nanjing 210042,China
关键词:
贝叶斯推理模型非线性系统时变监测
Keywords:
Bayesian inferring modelnonlinear systemtime-variantmonitoring
分类号:
TP13
摘要:
提出了采用贝叶斯推理模型BIM(Bayesian inferring model)对时变非线性系统的输出进行在线监测的实现思路和方法.首先描述了时变非线性系统的在线输出监测问题.然后介绍了BIM结构和训练方法,BIM的特点在于训练样本完全采自于在线闭环系统,采用改进的觅食优化算法IEFOA(Improved E.Coli Foraging Optimization Algorithm)离线训练门槛矩阵参数D.而在线预测应用时,采用滑动窗口数据实时更新BIM结构,从而实时跟踪系统的输出变化.最后,利用时变非线性对象对BIM的在线观测能力进行了验证,仿真结果表明BIM适合于系统的输出监测,并且具有设计简单、跟踪性能好等优点,为非线性系统的性能评估提供了一种新的底层数据预测方法.
Abstract:
The implementation idea and solution are proposed in this article for the output on-line monitoring of the timevariant nonlinear system by using bayesian inferring model ( BIM) . Firstly,the on-line monitoring problem of nonlinear system is described. Then the BIM structure and training methods are introduced. The characteristics of the BIM include that the sample data for off-line training are from the closed loop system and the optimization algorithm for the threshold matrix D is selected as the improved foraging optimization algorithm ( IEFOA) . While in the on-line applications,the sliding window data are used to update the structure of the BIM for the on-line tracing of the system output. The time-variant nonlinear object is employed to validate the on-line monitoring ability of the BIM. The simulation results indicate that the BIM is adapted to the system on-line output monitoring and owns the characteristics of easy design,high accuracy tracing ability and etc,which provide a kind of data prediction method for the lowest system.

参考文献/References:

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备注/Memo

备注/Memo:
基金项目: 国家自然科学基金( 60704024) 、江苏省普通高校自然科学研究计划( 10KJD510004) .通讯联系人: 刘益剑,博士,讲师,研究方向: 系统辨识与智能控制. E-mail: 63055@ njnu. edu. cn
更新日期/Last Update: 2013-03-11