ZHAO Yao-xin, ZHANG Guo-shan. Hybrid synchronization control of chaotic systems based on unbiased LS-SVM[J]. 2017, 36(11): 115-118+130. DOI: 10.13873/J.1000-9787(2017)11-0115-04.
基于无偏LS-SVM的混沌系统的混合同步控制
摘要
为实现超混沌系统与混沌系统的混合同步控制
通过对主系统采样
获得训练样本数集
设计了从系统混合同步轨线的形式;采用无偏最小二乘支持向量机(LS-SVM)的方法
引入核函数
获得优化目标
根据优化目标
求出混合同步轨线
设计了控制器。以超混沌Lorenz-Stenflo系统为主系统
Chen混沌系统为从系统为例
基于Matlab进行数值仿真
验证了方法的有效性。
Abstract
To realize hybrid synchronization control of hyper chaotic system and chaotic system
training sample number is obtained by sampling master system
and the hybrid synchronous trajectory of the slave system is set up. By using a method based on least square support vector machine( LS-SVM)
and introducing the kernel function
the optimization target can be obtained
and according to optimization target
hybrid synchronous trajectory is gotten
and the controller is designed. Taking the hyper chaotic Lorenz-Stenflo system as master system
and Chen chaotic system as slave system
numerical simulations based on Matlab is carried out and verify efficiency of the method.
Adaptive synchronization between different hyperchaotic systems with fully uncertain parameters [J] . Jian Huang. Physics Letters A . 2008 (27)
Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression [J] . Senjian An,Wanquan Liu,Svetha Venkatesh. Pattern Recognition . 2007 (8)
Support Vector Regression for the simultaneous learning of a multivariate function and its derivatives [J] . Marcelino Lázaro,Ignacio Santamaría,Fernando Pérez-Cruz,Antonio Artés-Rodríguez. Neurocomputing . 2005 (1)