摘要
针对时变非线性多输入多输出(MIMO)系统在线辨识较困难的问题,提出一种基于最小二乘支持向量机(LSSVM)的快速在线辨识方法。介绍了现有LSSVM增量式和在线式学习算法,并为它引入了一些加速实现策略,得到LSSVM快速在线式学习算法。将MIMO系统分解为多个多输入单输出(MISO)子系统,对每一个MISO利用一个LSSVM在线建模;这些LSSVM执行快速在线式学习算法。数字仿真显示该方法建模速度快,模型预测精度高。
To tackle the difficulty in identifying time-varying nonlinear Multi-Input Multi-Output （MIMO） system online, a fast online system identification approach based on Least Squares Support Vector Machine （LSSVM） was proposed. The existing LSSVM incremental and online learning algorithms were introduced, and some speeding up implementing tactics were designed and adopted in the algorithm; consequently, a fast online LSSVM learning algorithm was obtained. MIMO system was decomposed into multiple Multi-Input Single-Output （MISO） subsystems, and each MISO was modeled online via a LSSVM. The numerical simulation shows the modeling method is faster and the obtained models provide accurate prediction.
出处
《计算机应用》
CSCD
北大核心
2009年第8期2281-2284,2314,共5页
journal of Computer Applications
基金
国家自然科学基金资助项目(60872128)
国家技术创新基金资助项目(07C26214301740)
关键词
在线系统辨识
时变非线性系统
多输入多输出系统
最小二乘支持向量机
online system identification
time-varying nonlinear system
Multi-Input Multi-Output （MIMO） system
Least Squares Support Vector Machine （LSSVM）
