Title :
Global Exponential Stability of a Class of Neural Networks with Finite Distributed Delays
Author_Institution :
Yanshan Univ., Qinhuangdao
Abstract :
In this paper, global exponential stability of a class of neural networks with distributed delays is investigated by matrix measure technique and Halanay inequality. Several sufficient conditions are given to guarantee global exponential stability of the neural networks without assuming the differentiability of delay, the bound and the monotonicity of neuron activations. At last, two examples are given to illustrate the applicability of our results.
Keywords :
asymptotic stability; delays; neural nets; Halanay inequality; finite distributed delays; global exponential stability; matrix measure technique; neural networks; Associative memory; Cellular neural networks; Delay effects; Hopfield neural networks; Neural networks; Neurons; Output feedback; Signal processing; Stability; Sufficient conditions;
Conference_Titel :
Innovative Computing, Information and Control, 2007. ICICIC '07. Second International Conference on
Conference_Location :
Kumamoto
Print_ISBN :
0-7695-2882-1
DOI :
10.1109/ICICIC.2007.329
