New approach to global Mittag-Leffler synchronization problem of fractional-order quaternion-valued BAM neural networks based on a new inequality

2020 ◽  
Vol 122 ◽  
pp. 320-337 ◽  
Author(s):  
Jianying Xiao ◽  
Shiping Wen ◽  
Xujun Yang ◽  
Shouming Zhong
Keyword(s):  
Neural Networks ◽  
Fractional Order ◽  
Bam Neural Networks ◽  
New Approach ◽  
Synchronization Problem

scholarly journals Existence and Globally Asymptotic Stability of Equilibrium Solution for Fractional-Order Hybrid BAM Neural Networks with Distributed Delays and Impulses

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Hai Zhang ◽  
Renyu Ye ◽  
Jinde Cao ◽  
Ahmed Alsaedi
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Fractional Order ◽  
Lyapunov Functional ◽  
Equilibrium Solution ◽  
Distributed Delays ◽  
Bam Neural Networks ◽  
Network Systems ◽  
Globally Asymptotic Stability ◽  
Stability Of Equilibrium

This paper investigates the existence and globally asymptotic stability of equilibrium solution for Riemann-Liouville fractional-order hybrid BAM neural networks with distributed delays and impulses. The factors of such network systems including the distributed delays, impulsive effects, and two different fractional-order derivatives between the U-layer and V-layer are taken into account synchronously. Based on the contraction mapping principle, the sufficient conditions are derived to ensure the existence and uniqueness of the equilibrium solution for such network systems. By constructing a novel Lyapunov functional composed of fractional integral and definite integral terms, the globally asymptotic stability criteria of the equilibrium solution are obtained, which are dependent on the order of fractional derivative and network parameters. The advantage of our constructed method is that one may directly calculate integer-order derivative of the Lyapunov functional. A numerical example is also presented to show the validity and feasibility of the theoretical results.

Asymptotic anti-synchronization of memristor-based BAM neural networks with probabilistic mixed time-varying delays and its application

2018 ◽  
Vol 32 (24) ◽  
pp. 1850287 ◽  
Author(s):  
Manman Yuan ◽  
Weiping Wang ◽  
Xiong Luo ◽  
Lixiang Li
Keyword(s):  
Neural Networks ◽  
Secure Communication ◽  
Time Varying ◽  
Bam Neural Networks ◽  
Bidirectional Associative Memory ◽  
Stochastic Variable ◽  
Numerical Examples ◽  
Synchronization Problem ◽  
Encryption And Decryption ◽  
Adaptive Laws

This paper is concerned with the asymptotic anti-synchronization problem of the memristor-based bidirectional associative memory neural networks (MBAMNNs) and its application in network secure communication. First, we propose a new model of MBAMNNs with probabilistic delays. By establishing a Bernoulli distributed stochastic variable, the information of transmittal time-varying delays is studied. Second, in order to provide a more robust and secure system, we develop a new anti-synchronization model based on the MBAMNNs. The adaptive laws are carefully designed to confirm the process of encryption and decryption in networks secure communication system. Finally, several numerical examples are presented to demonstrate the effectiveness and applicability of our proposed mechanism.

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