Search

Article

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

Control of chaotic system based on least squares support vector machine modeling

Ye Mei-Ying

Citation:

Control of chaotic system based on least squares support vector machine modeling

Ye Mei-Ying
PDF
Get Citation

(PLEASE TRANSLATE TO ENGLISH

BY GOOGLE TRANSLATE IF NEEDED.)

  • A new approach to control chaotic systems is presented. This control approach is based on least squares support vector machines (LS_SVMs) modeling. Compared wit h the feed_forward neural networks, the LS_SVM possesses prominent advantages: o ver fitting is unlikely to occur by employing structural risk minimization crite rion, the global optimal solution can be uniquely obtained owing to the fact tha t its training is performed through the solution of a set of linear equations. A lso, the LS_SVM need not determine its topology in advance, which can be automat ically obtained when the training process ends. Thus the effectiveness and feasi bility of this method are found to be better than those of the feed_forward neur al networks. The method does not needs an analytic model, and it is still effect ive when there are measurement noises. The chaotic systems with one_and two_ dim ensional nonlinear maps are used as examples for demonstration.
Metrics
  • Abstract views:  6680
  • PDF Downloads:  833
  • Cited By: 0
Publishing process
  • Received Date:  13 February 2004
  • Accepted Date:  26 April 2004
  • Published Online:  31 December 2004

/

返回文章
返回