Exponential Lagrange stability for impulses in discrete-time delayed recurrent neural networks

ABSTRACT

This paper focuses on the problem of exponential stability in the sense of Lagrange for impulses in discrete-time delayed recurrent neural networks. By establishing a delayed impulsive discrete inequality and a novel difference inequality, combining with inequality techniques, some novel sufficient conditions are obtained to ensure exponential Lagrange stability for impulses in discrete-time delayed recurrent neural networks. Meanwhile, exponentially convergent scope of neural network is given. Finally, several numerical simulations are given to demonstrate the effectiveness of our results.

Keywords:

• Discrete-time
• impulses and delays
• Lagrange stability
• recurrent neural networks

Acknowledgments

The authors wish to thank the referees for their helpful comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors are grateful for the Youth Fund of Chongqing Three Gorges University [grant number 16QN14], and the support of the National Natural Science Foundation of China [grant number 11601047], Project Supported by Chongqing Municipal Key Laboratory of Institutions of Higher Education [grant number [2017]3].