Exponential Stabilization of Time‐varying Delayed Complex‐valued Memristor‐based Neural Networks Via Impulsive Control

This paper focuses on the exponential stabilization problem for Markov jump neural networks with Time-varying Delays (TDs). Firstly, we provide a new Free-matrix-based Exponential-type Integral Inequality (FMEII) containing the information of attenuation exponent, which is helpful to reduce the conservativeness of stability criteria. To further save control cost, we introduce a sample-based Adaptive Event-triggered Impulsive Control (AEIC) scheme, in which the trigger threshold is adaptively varied with the sampled state. By fully considering the information about sampled state, TDs, and Markov jump parameters, a suitable Lyapunov–Krasovskii functional is constructed. With the virtue of FMEII and AEIC scheme, some novel stabilization criteria are presented in the form of linear matrix inequalities. At last, two numerical examples are given to show the validity of the obtained results.

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Finite-time projective synchronization of stochastic complex-valued neural networks with probabilistic time-varying delays

IEEE Access ◽

10.1109/access.2021.3066585 ◽

2021 ◽

pp. 1-1

Author(s):

Meng Hui ◽

Jiahuang Zhang ◽

Jiao Zhang ◽

Herbert Ho-Ching Iu ◽

Rui Yao ◽

...

Keyword(s):

Neural Networks ◽

Finite Time ◽

Projective Synchronization ◽

Time Varying ◽

Complex Valued

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On the stability with respect to manifolds of reaction-diffusion impulsive control fractional-order neural networks with time-varying delays

10.1063/5.0041946 ◽

2021 ◽

Author(s):

Gani Stamov ◽

Trayan Stamov ◽

Ivanka Stamova

Keyword(s):

Neural Networks ◽

Fractional Order ◽

Impulsive Control ◽

Reaction Diffusion ◽

Time Varying ◽

The Stability

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Impulsive Disturbances on the Dynamical Behavior of Complex-Valued Cohen-Grossberg Neural Networks with Both Time-Varying Delays and Continuously Distributed Delays

Complexity ◽

10.1155/2017/3826729 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 5

Author(s):

Xiaohui Xu ◽

Jiye Zhang ◽

Quan Xu ◽

Zilong Chen ◽

Weifan Zheng

Keyword(s):

Neural Networks ◽

Exponential Stability ◽

Equilibrium Point ◽

Global Exponential Stability ◽

Dynamical Behavior ◽

Sufficient Conditions ◽

Distributed Delays ◽

Time Varying ◽

Homeomorphism Mapping ◽

Complex Valued

This paper studies the global exponential stability for a class of impulsive disturbance complex-valued Cohen-Grossberg neural networks with both time-varying delays and continuously distributed delays. Firstly, the existence and uniqueness of the equilibrium point of the system are analyzed by using the corresponding property of M-matrix and the theorem of homeomorphism mapping. Secondly, the global exponential stability of the equilibrium point of the system is studied by applying the vector Lyapunov function method and the mathematical induction method. The established sufficient conditions show the effects of both delays and impulsive strength on the exponential convergence rate. The obtained results in this paper are with a lower level of conservatism in comparison with some existing ones. Finally, three numerical examples with simulation results are given to illustrate the correctness of the proposed results.

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