Module-phase synchronization of fractional-order complex chaotic systems based on RBF neural network and sliding mode control

被引：6

作者：

Nian, Fuzhong ^{[1
]}

Liu, Xinmeng ^{[1
]}

Zhang, Yaqiong ^{[1
]}

Yu, Xuelong ^{[1
]}

机构：

[1] Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Peoples R China

来源：

INTERNATIONAL JOURNAL OF MODERN PHYSICS B | 2020年 / 34卷 / 07期

基金：

中国国家自然科学基金;

关键词：

RBF neural network; sliding mode control; fractional-order complex chaotic systems; synchronization; TIME;

D O I：

10.1142/S0217979220500502

中图分类号：

O59 [应用物理学];

学科分类号：

摘要：

Combined with RBF neural network and sliding mode control, the synchronization between drive system and response system was achieved in module space and phase space, respectively (module-phase synchronization). The RBF neural network is used to estimate the unknown nonlinear function in the system. The module-phase synchronization of two fractional-order complex chaotic systems is implemented by the Lyapunov stability theory of fractional-order systems. Numerical simulations are provided to show the effectiveness of the analytical results.

引用

页数：21

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