Improved delay-dependent exponential stability criteria for discrete-time recurrent neural networks with time-varying delays

作者：Zhang Baoyong^*; Xu Shengyuan; Zou Yun

来源：Neurocomputing, 2008, 72(1-3): 321-330.

DOI：10.1016/j.neucom.2008.01.006

• Delay-dependent stability
• Discrete-time neural networks
• Global exponential stability
• Linear matrix inequality
• Time-varying delays

This paper is concerned with the problem of stability analysis for a class of discrete-time recurrent neural networks with time-varying delays. Under a weak assumption on the activation functions and using a new Lyapunov functional, a delay-dependent condition guaranteeing the global exponential stability of the concerned neural network-is obtained in terms of a linear matrix inequality. It is shown that this stability condition is less conservative than some previous ones in the literature. When norm-bounded parameter uncertainties appear in a delayed discrete-time recurrent neural network, a delay-dependent robust exponential stability criterion is also presented. Numerical examples are provided to demonstrate the effectiveness of the proposed method.

• 出版日期2008-12
• 单位南京理工大学