IDENTIFICATION OF CHAOTIC SYSTEMS WITH NOISY DATA BASED ON RBF NEURAL NETWORKS

被引：0

作者：

Li, Dong-Mei ^{[1
]}

Li, Fa-Chao ^{[1
]}

机构：

[1] Hebei Univ Sci & Technol, Sch Econ & Management, Shijiazhuang 050018, Peoples R China

来源：

PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-6 | 2009年

关键词：

Rbf neural networks; Chaotic systems identification; Noisy chaotic systems; WIENER;

D O I：

10.1109/ICMLC.2009.5212655

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we present that noisy chaotic systems can be identified with RBF neural networks. We design three-layers RBF network structure and clarify fundamental properties of RBF networks to learn noisy chaotic systems by some numerical experiments. We also evaluate the identified models with reconstruction of attractors by the identified models. Simulations show that the identified models can approach to original chaotic systems and extract dynamical characteristics of original chaotic systems.

引用

页码：2578 / 2581

页数：4

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