Delay-dependent robust exponential stability for uncertain recurrent neural networks with time-varying delays.
This paper considers the problem of robust exponential stability for a class of recurrent neural networks with time-varying delays and parameter uncertainties. The time delays are not necessarily differentiable and the uncertainties are assumed to be time-varying but norm-bounded. Sufficient conditions, which guarantee that the concerned uncertain delayed neural network is robustly, globally, exponentially stable for all admissible parameter uncertainties, are obtained under a weak assumption on the neuron activation functions. These conditions are dependent on the size of the time delay and expressed in terms of linear matrix inequalities. Numerical examples are provided to demonstrate the effectiveness and less conservatism of the proposed stability results.
Zhang
Baoyong
B
School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, P. R. China. baoyongzhang@yahoo.com.cn
Xu
Shengyuan
S
Li
Yongmin
Y
D003198:Computer Simulation; D008433:Mathematics; D008959:Models, Neurological; D016571:Neural Networks, Computer; D013997:Time Factors; D035501:Uncertainty