Projective synchronization of different chaotic neural networks with mixed time delays based on an integral sliding mode controller

2014 ◽  
Vol 123 ◽  
pp. 443-449 ◽  
Author(s):  
Yanchao Shi ◽  
Peiyong Zhu ◽  
Ke Qin
Keyword(s):  
Neural Networks ◽  
Time Delays ◽  
Sliding Mode ◽  
Projective Synchronization ◽  
Sliding Mode Controller ◽  
Chaotic Neural Networks ◽  
Integral Sliding Mode ◽  
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.

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

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 Projective Synchronization of Nonidentical Fractional-Order Memristive Neural Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Chong Chen ◽  
Zhixia Ding
Keyword(s):  
Neural Networks ◽  
Fractional Order ◽  
Sliding Mode ◽  
Direct Method ◽  
Projective Synchronization ◽  
Sliding Mode Controller ◽  
Memristive Neural Networks ◽  
Lyapunov Direct Method ◽  
Sliding Motion ◽  
Fractional Order Integral

This paper investigates projective synchronization of nonidentical fractional-order memristive neural networks (NFMNN) via sliding mode controller. Firstly, based on the sliding mode control theory, a new fractional-order integral sliding mode controller is designed to ensure the occurrence of sliding motion. Furthermore, according to fractional-order differential inequalities and fractional-order Lyapunov direct method, the trajectories of the system converge to the sliding mode surface to carry out sliding mode motion, and some sufficient criteria are obtained to achieve global projective synchronization of NFMNN. In addition, the conclusions extend and improve some previous works on the synchronization of fractional-order memristive neural networks (FMNN). Finally, a simulation example is given to verify the effectiveness and correctness of the obtained results.

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