Projective synchronization adaptive control for different chaotic neural networks with mixed time delays

Optik ◽  
2016 ◽  
Vol 127 (5) ◽  
pp. 2551-2557 ◽  
Author(s):  
Yong-Qing Fan ◽  
Ke-Yi Xing ◽  
Yin-He Wang ◽  
Li-Yang Wang
Keyword(s):  
Neural Networks ◽  
Adaptive Control ◽  
Time Delays ◽  
Projective Synchronization ◽  
Chaotic Neural Networks ◽  
Mixed Time Delays

Improved Control Schemes for Projective Synchronization of Delayed Neural Networks with Unmatched Coefficients

2019 ◽  
Vol 34 (03) ◽  
pp. 2051005 ◽  
Author(s):  
Abdujelil Abdurahman ◽  
Haijun Jiang
Keyword(s):  
Neural Networks ◽  
Time Delays ◽  
Chaos Synchronization ◽  
Projective Synchronization ◽  
Chaotic Neural Networks ◽  
Delayed Neural Networks ◽  
Mixed Time Delays ◽  
Control Schemes ◽  
Master System ◽  
Inequality Techniques

Projective synchronization (PS) is a type of chaos synchronization where the states of slave system are scaled replicas of the states of master system. This paper studies the asymptotic projective synchronization (APS) between master–slave chaotic neural networks (NNs) with mixed time-delays and unmatched coefficients. Based on useful inequality techniques and constructing a suitable Lyapunov functional, some simple criteria are derived to ensure the APS of considered networks via designing a novel adaptive feedback controller. In addition, a numerical example and its MATLAB simulations are provided to check the feasibility of the obtained results. The main innovation of our work is that we dealt with the APS problem between two different chaotic NNs, while most of the existing works only concerned with the PS of chaotic systems with the same topologies. In addition, compared with the controllers introduced in the existing papers, the designed controller in this paper does not require any knowledge about the activation functions, which can be seen as another novelty of the paper.

ADAPTIVE SYNCHRONIZATION FOR UNKNOWN STOCHASTIC CHAOTIC NEURAL NETWORKS WITH MIXED TIME-DELAYS BY OUTPUT COUPLING

2008 ◽  
Vol 22 (24) ◽  
pp. 2391-2409 ◽  
Author(s):  
YANG TANG ◽  
JIAN-AN FANG ◽  
SUOJUN LU ◽  
QINGYING MIAO
Keyword(s):  
Neural Networks ◽  
Time Delays ◽  
Distributed Delay ◽  
Adaptive Synchronization ◽  
Output Coupling ◽  
Stochastic Neural Networks ◽  
Unknown Parameters ◽  
Chaotic Neural Networks ◽  
Rigorous Approach ◽  
Mixed Time Delays

This paper is concerned with the synchronization problem for a class of stochastic neural networks with unknown parameters and mixed time-delays via output coupling. The mixed time-delays comprise the time-varying delay and distributed delay, and the neural networks are subjected to stochastic disturbances described in terms of a Brownian motion. Firstly, we use Lyapunov functions to establish general theoretical conditions for designing the output coupling matrix. Secondly, by using the adaptive feedback technique, a simple, analytical and rigorous approach is proposed to synchronize the stochastic neural networks with unknown parameters and mixed time-delays. Finally, numerical simulation results are given to show the effectiveness of the proposed method.

scholarly journals Generalized Projective Synchronization between Two Different Neural Networks with Mixed Time Delays

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Xuefei Wu ◽  
Chen Xu ◽  
Jianwen Feng ◽  
Yi Zhao ◽  
Xuan Zhou
Keyword(s):  
Neural Networks ◽  
Time Delays ◽  
Lyapunov Stability Theory ◽  
Projective Synchronization ◽  
Nonlinear Coupling ◽  
Nonlinear Feedback ◽  
Nonlinear Controller ◽  
Feedback Controllers ◽  
Mixed Time Delays ◽  
Generalized Projective Synchronization

The generalized projective synchronization (GPS) between two different neural networks with nonlinear coupling and mixed time delays is considered. Several kinds of nonlinear feedback controllers are designed to achieve GPS between two different such neural networks. Some results for GPS of these neural networks are proved theoretically by using the Lyapunov stability theory and the LaSalle invariance principle. Moreover, by comparison, we determine an optimal nonlinear controller from several ones and provide an adaptive update law for it. Computer simulations are provided to show the effectiveness and feasibility of the proposed methods.

Sign in / Sign up

Export Citation Format

Share Document