Uniform Stability of Fractional-order Memristive Neural Networks with Hybrid Time-Varying Delays

2021 ◽  
Author(s):  
Xiaofang Hu ◽  
Leimin Wang
Keyword(s):  
Neural Networks ◽  
Fractional Order ◽  
Uniform Stability ◽  
Time Varying ◽  
Memristive Neural Networks

Delay-dependent criterion for asymptotic stability of a class of fractional-order memristive neural networks with time-varying delays

2019 ◽  
Vol 118 ◽  
pp. 289-299 ◽  
Author(s):  
Liping Chen ◽  
Tingwen Huang ◽  
J.A. Tenreiro Machado ◽  
António M. Lopes ◽  
Yi Chai ◽  
...  
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Fractional Order ◽  
Time Varying ◽  
Memristive Neural Networks ◽  
Delay Dependent

scholarly journals Finite-Time Projective Synchronization of Fractional-Order Memristive Neural Networks with Mixed Time-Varying Delays

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-27
Author(s):  
Meng Hui ◽  
Chen Wei ◽  
Jiao Zhang ◽  
Herbert Ho-Ching Iu ◽  
Ni Luo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Lyapunov Function ◽  
Fractional Order ◽  
Finite Time ◽  
Settling Time ◽  
Projective Synchronization ◽  
Time Varying ◽  
Memristive Neural Networks ◽  
Synchronization Problem ◽  
Fractional Differential

This paper is concerned with the finite-time projective synchronization problem of fractional-order memristive neural networks (FMNNs) with mixed time-varying delays. Firstly, under the frame of fractional-order differential inclusion and the set-valued map, several criteria are derived to ensure finite-time projective synchronization of FMNNs. Meanwhile, three properties are established to deal with different forms of the finite-time fractional differential inequation, which greatly extend some results on estimation of settling time of FMNNs. In addition to the traditional Lyapunov function with 1-norm form in Theorem 1, a more general and flexible Lyapunov function based on p-norm is constructed in Theorem 2 to analyze the finite-time projective synchronization problem, and the estimation of settling time has been verified less conservative than previous results. Finally, numerical examples are provided to demonstrate the effectiveness of the derived theoretical results.

scholarly journals Global Asymptotic Synchronization of Fractional Order Multi-linked Memristive Neural Networks with Time-varying Delays via Discontinuous Control

2021 ◽  
Author(s):  
Shaofang Wang ◽  
Lixiang Li ◽  
Haipeng Peng ◽  
Yixian Yang ◽  
Mingwen Zheng
Keyword(s):  
Neural Networks ◽  
Fractional Order ◽  
Time Varying ◽  
Memristive Neural Networks ◽  
Feedback Controllers ◽  
Set Valued Mapping ◽  
The Right ◽  
First Time ◽  
Asymptotic Synchronization ◽  
Global Asymptotic Synchronization

In this paper, we address the global asymptotic synchronization (GAS) problem of the Master-Slave fractional order multi-linked memristive neural networks (FOMMNNs). Firstly, we propose the FOMMNNs which incorporate the fractional calculus into multi-linked memristive neural networks (MMNNs) for the first time. Then, by utilizing the fractional differential inclusions and set-valued mapping theories, the addressed FOMMNNs with discontinuous state switching at the right-hand side and time-varying delays are converted into the continuous FOMMNNs. Under the frameworks of fractional Caputo derivative and fractional Fillipov solution, by the way of building up appropriate Lyapunov functionals and utilizing some synchronous analysis technology, several sufficient criteria ensuring that the Master-Slave FOMMNNs can realize global asymptotic synchronization (GAS) under two different state-feedback controllers are obtained.

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