| 基于RBF网络的动力系统Lyapunov指数的计算方法 |
| |
| 引用本文： | 李冬梅,王正欧.基于RBF网络的动力系统Lyapunov指数的计算方法[J].信息与控制,2004,33(5):523-526. |
| |
| 作者姓名： | 李冬梅 王正欧 |
| |
| 作者单位： | 1. 河北科技大学经济管理学院,河北,石家庄,050018
2. 天津大学系统工程研究所,天津,300072 |
| |
| 摘 要： | 提出一种基于径向基函数(RBF)神经网络的动力系统Lyapunov指数计算方法，设计了一个RBF网络结构，推导了基于RBF网络的Lyapunov指数计算公式．仿真实验表明，与其它现有方法相比，此方法计算精度较高，收敛速度较快，而且只需要较少的样本数据量．本方法能更准确、更快速地计算动力系统的Lyapunov指数．
|
| 关 键 词： | Lyapunov指数 RBF神经网络 动力系统辨识 非线性系统 |
| 文章编号： | 1002-0411(2004)05-0523-04 |
An Algorithm for Computing Lyapunov Exponents of a Dynamical System Based on RBF Neural Networks |
| |
| LI Dong-mei,WANG Zheng-ou.An Algorithm for Computing Lyapunov Exponents of a Dynamical System Based on RBF Neural Networks[J].Information and Control,2004,33(5):523-526. |
| |
| Authors: | LI Dong-mei WANG Zheng-ou |
| |
| Affiliation: | LI Dong-mei1,WANG Zheng-ou2 |
| |
| Abstract: | An algorithm for computing Lyapunov exponents of a dynamical system based on radial basis function(RBF) neural networks is proposed. An RBF network structure is designed. The formula of the Lyapunov exponents based on an RBF network is derived. Simulations show that compared with the other existing algorithms, the proposed algorithm has higher accuracy and convergence speed, and it needs much less observed samples. It is demonstrated that the proposed algorithm can compute the Lyapunov exponents of a dynamical system more accurately and rapidly. |
| |
| Keywords: | Lyapunov exponents RBF(radial basis function) neural networks dynamical systems identification nonlinear system |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
