New stability results of neutral-type neural networks with continuously distributed delays and impulses

Abstract

This paper studies the globally asymptotic stability of neutral-type impulsive neural networks with discrete and bounded continuously distributed delays. By using the Lyapunov functional method, quadratic convex combination approach, a novel Gu's Lemma, Jensen integral inequality and linear convex combination technique, several novel sufficient conditions are derived to ensure the globally asymptotic stability of the equilibrium point of the networks. The proposed results, which do not require the differentiability and monotonicity of the activation functions, can be easily checked via Matlab software. Finally, two numerical examples are given to demonstrate the effectiveness of our theoretical results.

Keywords:

• impulse
• neutral-type neural networks
• Jensen integral inequality
• linear convex combination
• quadratic convex combination

2010 AMS Subject Classifications:

• 92B20
• 34D20
• 34D23
• 34K50
• 34K40

Acknowledgements

This work was supported by the National Natural Science Foundation of China 61034005, 61074073, 61273022, Program for New Century Excellent Talents in University of China (NCET-10-0306), and the Fundamental Research Funds for the Central Universities under Grants N110504001 and N100104102.