scholarly journals Projective synchronization analysis for BAM neural networks with time-varying delay via novel control

2021 ◽  
Vol 26 (1) ◽  
pp. 41-56
Author(s):  
Malika Sader ◽  
Fuyong Wang ◽  
Zhongxin Liu ◽  
Zhongxin Chen
Keyword(s):  
Neural Networks ◽  
Adaptive Controller ◽  
Projective Synchronization ◽  
Time Varying ◽  
Bam Neural Networks ◽  
Application Process ◽  
Time Varying Delay ◽  
Synchronization Problem ◽  
Varying Delay ◽  
Theoretical Results

In this paper, the projective synchronization of BAM neural networks with time-varying delays is studied. Firstly, a type of novel adaptive controller is introduced for the considered neural networks, which can achieve projective synchronization. Then, based on the adaptive controller, some novel and useful conditions are obtained to ensure the projective synchronization of considered neural networks. To our knowledge, different from other forms of synchronization, projective synchronization is more suitable to clearly represent the nonlinear systems’ fragile nature. Besides, we solve the projective synchronization problem between two different chaotic BAM neural networks, while most of the existing works only concerned with the projective synchronization chaotic systems with the same topologies. Compared with the controllers in previous papers, the designed controllers in this paper do not require any activation functions during the application process. Finally, an example is provided to show the effectiveness of the theoretical results.

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.

Robust state estimation for discrete-time BAM neural networks with time-varying delay

2014 ◽  
Vol 131 ◽  
pp. 171-178 ◽  
Author(s):  
A. Arunkumar ◽  
R. Sakthivel ◽  
K. Mathiyalagan ◽  
S. Marshal Anthoni
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Discrete Time ◽  
Time Varying ◽  
Bam Neural Networks ◽  
Time Varying Delay ◽  
Varying Delay

State estimation for switched discrete-time stochastic BAM neural networks with time varying delay

2013 ◽  
Vol 73 (3) ◽  
pp. 1565-1585 ◽  
Author(s):  
A. Arunkumar ◽  
R. Sakthivel ◽  
K. Mathiyalagan ◽  
S. Marshal Anthoni
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Discrete Time ◽  
Time Varying ◽  
Bam Neural Networks ◽  
Time Varying Delay ◽  
Varying Delay

scholarly journals Synchronization Analysis for Stochastic Inertial Memristor-Based Neural Networks with Linear Coupling

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Lixia Ye ◽  
Yonghui Xia ◽  
Jin-liang Yan ◽  
Haidong Liu
Keyword(s):  
Neural Networks ◽  
Sufficient Conditions ◽  
Stochastic Perturbation ◽  
Parametric Uncertainty ◽  
Uncertain System ◽  
Linear Coupling ◽  
Time Varying Delay ◽  
Synchronization Problem ◽  
Varying Delay ◽  
Theoretical Results

This paper concerns the synchronization problem for a class of stochastic memristive neural networks with inertial term, linear coupling, and time-varying delay. Based on the interval parametric uncertainty theory, the stochastic inertial memristor-based neural networks (IMNNs for short) with linear coupling are transformed to a stochastic interval parametric uncertain system. Furthermore, by applying the Lyapunov stability theorem, the stochastic analysis approach, and the Halanay inequality, some sufficient conditions are obtained to realize synchronization in mean square. The established criteria show that stochastic perturbation is designed to ensure that the coupled IMNNs can be synchronized better by changing the state coefficients of stochastic perturbation. Finally, an illustrative example is presented to demonstrate the efficiency of the theoretical results.

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