Design of controller on synchronization of chaotic neural networks with mixed time-varying delays

Authors:
Qiankun Song
Affiliations:
Yangtze Center of Mathematics, Sichuan University, Chengdu 610064, China and Department of Mathematics, Chongqing Jiaotong University, Chongqing 400074, China
Venue:
Neurocomputing
Year:
2009

Citing 7
Cited 5

Global exponential stability of generalized recurrent neural networks with discrete and distributed delays

Neural Networks
Exponential synchronization of stochastic perturbed chaotic delayed neural networks

Neurocomputing
Letters: Exponential synchronization of chaotic neural networks with mixed delays

Neurocomputing
On synchronization of coupled neural networks with discrete and unbounded distributed delays

International Journal of Computer Mathematics - COMPLEX NETWORKS
Synchronization of neural networks based on parameter identification and via output or state coupling

Journal of Computational and Applied Mathematics
State estimation for jumping recurrent neural networks with discrete and distributed delays

Neural Networks
Hybrid chaos synchronization and its application in information processing

Mathematical and Computer Modelling: An International Journal

Exponential synchronization of neural networks with time-varying mixed delays and sampled-data

Neurocomputing
Letters: Synchronization of stochastic Markovian jump neural networks with reaction-diffusion terms

Neurocomputing
Global exponential synchronization of generalized stochastic neural networks with mixed time-varying delays and reaction-diffusion terms

Neurocomputing
Exponential synchronization of chaotic neural networks with mixed delays and impulsive effects via output coupling with delay feedback

Mathematical and Computer Modelling: An International Journal
Synchronization of discrete-time neural networks with time delays subject to missing data

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, the problem on synchronization is investigated for neural networks with discrete and distributed time-varying delays as well as generalized activation functions. By constructing proper Lyapunov-Krasovskii functional and employing a combination of the free-weighting matrix method, Newton-Leibniz formulation and inequality technique, the controllers are, respectively, designed to achieve the asymptotical and exponential synchronization of the addressed neural networks. The provided conditions are expressed in terms of LMIs, and are dependent on both the discrete and distributed time delays. A simulation example is given to show the effectiveness and less conservatism of the obtained conditions. It is noteworthy that the traditional assumptions on the differentiability of the time-varying delays and the boundedness of its derivative are removed.