Improved conditions for global exponential stability of recurrent neural networks with time-varying delays.
This paper presents new theoretical results on global exponential stability of recurrent neural networks with bounded activation functions and time-varying delays. The stability conditions depend on external inputs, connection weights, and time delays of recurrent neural networks. Using these results, the global exponential stability of recurrent neural networks can be derived, and the estimated location of the equilibrium point can be obtained. As typical representatives, the Hopfield neural network (HNN) and the cellular neural network (CNN) are examined in detail.
Zeng
Zhigang
Z
School of Automation, Wuhan University of Technology, Wuhan, Hubei 430070, China. zhigangzeng@163.com
Wang
Jun
J
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D013598:Systems Theory; D013997:Time Factors