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.

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.

scholarly journals Robust Stabilization andH∞Control for Uncertain Neural Networks with Mixed Time Delays

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Bin Wen ◽  
Hui Li ◽  
Li Liang
Keyword(s):  
Neural Networks ◽  
Robust Stability ◽  
Time Delays ◽  
Convex Combination ◽  
Robust Stabilization ◽  
Sufficient Conditions ◽  
Lyapunov Stability Theory ◽  
Linear Matrix ◽  
Mixed Time Delays ◽  
Uncertain Neural Networks

This paper is concerned with the problem of robust stabilization andH∞control for a class of uncertain neural networks. For the robust stabilization problem, sufficient conditions are derived based on the quadratic convex combination property together with Lyapunov stability theory. The feedback controller we design ensures the robust stability of uncertain neural networks with mixed time delays. We further design a robustH∞controller which guarantees the robust stability of the uncertain neural networks with a givenH∞performance level. The delay-dependent criteria are derived in terms of LMI (linear matrix inequality). Finally, numerical examples are provided to show the effectiveness of the obtained results.

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

General decay projective synchronization of memristive competitive neural networks via nonlinear controller

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Malika Sader ◽  
Fuyong Wang ◽  
Zhongxin Liu ◽  
Zengqiang Chen
Keyword(s):  
Neural Networks ◽  
General Decay ◽  
Projective Synchronization ◽  
Communication Security ◽  
Activation Functions ◽  
Nonlinear Feedback ◽  
Nonlinear Controller ◽  
Control Scheme ◽  
Special Cases ◽  
Inequality Techniques

Abstract In this paper, the general decay projective synchronization of a class of memristive competitive neural networks with time delay is studied. Firstly, a nonlinear feedback controller is designed, which does not require any knowledge about the activation functions. Then, some new and applicable conditions dependent on the Lyapunov function and the inequality techniques are obtained to guarantee the general decay projective synchronization of the considered systems under the developed controller. Unlike other forms of synchronization, projective synchronization can improve communication security due to the scaling constant’s unpredictability. In addition, the polynomial synchronization, asymptotical synchronization, and exponential synchronization can be seen as the special cases of the general decay projective synchronization. Finally, a numerical example is given to demonstrate the effectiveness of the proposed control scheme.

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