Global stability analysis in Hopfield neural networks
ZHANG Ji-ye
DAI Huan-yun
WU Ping-bo
Abstract：The existence and uniqueness of the equilibrium and the global attractivity of Hopfield neural network models are investigated. Instead of assuming the boundedness, monotonicity and differentiability of the activation functions and by using M-matrix theory, Lyapunov functions are constructed and employed to establish sufficient conditions for global asymptotic stability.
Keywords：neural networkglobal asymptotic stabilityM-matrix
Publication Date：2003-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 180-184 )