Some stability properties of recurrent neural networks
Jennie Si, Ching‐Fang Lin · 2005
Stability properties of recurrent neural networks are investigated using Lyapunov stability theory and functional analytic means. Sufficient conditions for the global asymptotic stability and exponentially asymptotic stability of equilibrium points of a class of recurrent neural networks are provided. The results obtained can be applied when recurrent neural networks are used as computation models, in particular as optimization models. The results may also be used as stability analysis tools for some class of nonlinear control systems.