计算机应用 ›› 2009, Vol. 29 ›› Issue (08): 2281-2284.

• 典型应用 • 上一篇    下一篇

基于LSSVM的MIMO系统快速在线辨识方法

周欣然1,滕召胜2,赵新闻2   

  1. 1. 中南大学 信息科学与工程学院;湖南大学 电气与信息工程学院
    2.
  • 收稿日期:2009-02-13 修回日期:2009-03-31 发布日期:2008-08-01 出版日期:2009-08-01
  • 通讯作者: 周欣然
  • 基金资助:
    国家级基金

Fast online system identification for MIMO using LSSVM

  • Received:2009-02-13 Revised:2009-03-31 Online:2008-08-01 Published:2009-08-01

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。

关键词: 最小二乘支持向量机, 在线系统辨识, 时变非线性系统, 多输入多输出系统, Least Squares Support Vector Machines (LSSVM), online system identification, time-varying nonlinear system, Multi-Input Multi-Output (MIMO) system

Abstract: 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.

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