Adaptive Projective Synchronization and Function Projective Synchronization of Chaotic Neural Networks with Delayed and Non-delayed Coupling

2012 ◽  
pp. 293-301 ◽  
Author(s):  
Guoliang Cai ◽  
Hao Ma ◽  
Yuxiu Li
Keyword(s):  
Neural Networks ◽  
Projective Synchronization ◽  
Chaotic Neural Networks ◽  
Function Projective Synchronization ◽  
Delayed Coupling ◽  
And Function

MODIFIED FUNCTION PROJECTIVE SYNCHRONIZATION OF CHAOTIC NEURAL NETWORKS WITH DELAYS BASED ON OBSERVER

2011 ◽  
Vol 22 (02) ◽  
pp. 169-180 ◽  
Author(s):  
LIPING CHEN ◽  
YI CHAI ◽  
RANCHAO WU
Keyword(s):  
Neural Networks ◽  
Scaling Function ◽  
Neural Model ◽  
Cellular Neural Network ◽  
State Observer ◽  
Projective Synchronization ◽  
Chaotic Neural Networks ◽  
Function Projective Synchronization ◽  
Modified Function Projective Synchronization ◽  
Function Matrix

This paper deals with a new type of synchronization scheme for chaotic neural networks with delays, called modified function projective synchronization, in which chaotic neural networks synchronize up to a scaling function matrix. Based on the nonlinear state observer, a control scheme is derived through the placement technique by designing a state-observer of the derived system to synchronize chaotic neural networks up to a scaling function matrix. This technique, capable of adjusting the scaling function factor arbitrarily for the value of scaling function factor, has no effect on the controllability of error system. That overcomes some limitation in earlier literature. A chaotic cellular neural network and a chaotic Hopfield neural model are used as numerical examples to demonstrate the effectiveness of the proposed synchronization technique.

Exponential lag function projective synchronization of memristor-based multidirectional associative memory neural networks via hybrid control

2018 ◽  
Vol 32 (09) ◽  
pp. 1850116 ◽  
Author(s):  
Manman Yuan ◽  
Weiping Wang ◽  
Xiong Luo ◽  
Lixiang Li ◽  
Jürgen Kurths ◽  
...  
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Time Delays ◽  
Hybrid Control ◽  
Projective Synchronization ◽  
Mixed Delays ◽  
Time Varying ◽  
Function Projective Synchronization ◽  
Different Types ◽  
Discrete Delays

This paper is concerned with the exponential lag function projective synchronization of memristive multidirectional associative memory neural networks (MMAMNNs). First, we propose a new model of MMAMNNs with mixed time-varying delays. In the proposed approach, the mixed delays include time-varying discrete delays and distributed time delays. Second, we design two kinds of hybrid controllers. Traditional control methods lack the capability of reflecting variable synaptic weights. In this paper, the controllers are carefully designed to confirm the process of different types of synchronization in the MMAMNNs. Third, sufficient criteria guaranteeing the synchronization of system are derived based on the derive-response concept. Finally, the effectiveness of the proposed mechanism is validated with numerical experiments.

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.

Improved Results on Adaptive Control Approach for Projective Synchronization of Neural Networks with Time-Varying Delay

2019 ◽  
Vol 20 (6) ◽  
pp. 623-631 ◽  
Author(s):  
Abdujelil Abdurahman ◽  
Malika Sader ◽  
Haijun Jiang
Keyword(s):  
Neural Networks ◽  
Functional Method ◽  
Mean Value ◽  
Projective Synchronization ◽  
Mean Value Theorem ◽  
Time Varying ◽  
Chaotic Neural Networks ◽  
Control Approach ◽  
Time Varying Delay ◽  
Varying Delay

AbstractCompared to other types of synchronization such as complete synchronization and lag synchronization, there is a unique advantage in projective synchronization since it can greatly improve the security of communication. In this paper, the projective synchronization problem of a class of chaotic neural networks with time-varying delay is investigated via designing a novel adaptive controller. Some simple and useful criteria are derived by employing Lyapunov functional method and Lagrange mean value theorem. Finally, an example and its numerical simulations are given to demonstrate the effectiveness of the proposed control schemes. It is worth to mention that the designed controller in this paper dos not require any knowledge about the activation functions, which can be seen the main novelty of the paper.

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