Delay-interval-dependent robust-stability criteria for neutral stochastic neural networks with polytopic and linear fractional uncertainties

Abstract

In this paper, the delay-interval-dependent robust stability is studied for a class of neutral stochastic neural networks with time-varying delays. The time-varying delay is assumed to belong to an interval, which means that the upper bound is known and the lower bound is not restricted to zero. For the neural networks under study, the uncertainty includes polytopic uncertainty and linear fractional norm-bounded uncertainty. Sufficient conditions for the stability of the addressed neutral stochastic neural networks with time-varying delays are established by employing the proper Lyapunov–Krasovskii functional, a combination of the stochastic analysis theory, some inequality techniques and new linear matrix inequality (LMI). Finally, three numerical examples are provided to demonstrate less conservatism and effectiveness of the proposed LMI conditions.

Keywords:

• delay-interval-dependent stability
• linear matrix inequality
• Lyapunov–Krasovskii functional
• neutral stochastic neural networks

2010 AMS Subject Classifications :

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

Acknowledgement

The authors are very much thankful to the referees for their valuable comments and suggestions for improving this manuscript. The work of authors was supported by Department of Science and Technology, New Delhi India under the sanctioned No. SR/S4/MS:485/07.