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.
The implementation idea and solution are proposed in this article for the output on-line monitoring of the time- variant 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-va- riant 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.
出处
《南京师范大学学报（工程技术版）》
CAS
2012年第2期7-10,共4页
Journal of Nanjing Normal University(Engineering and Technology Edition)
基金
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)
关键词
贝叶斯推理模型
非线性系统
时变
监测
Bayesian inferring model, nonlinear system, time-variant, monitoring
