Controlling chaotic systems via support vector machines without analytical model

Authors:
Meiying Ye
Affiliations:
College of Mathematics and Physics, Zhejiang Normal University, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

Citing 2
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
New Support Vector Algorithms

Neural Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

A controlling chaotic system method based on support vector machines (SVMs) is proposed. The method has been tested for controlling the Hénon map from arbitrary initial states to the desirable stationary point or function input without the need of an analytic model. We can see that its performance is very good in simulation studies. Even if there is additive noise, the proposed method is still effective.