Global asymptotic stability of a class of dynamical neural networks

In this paper, we present a sufficient condition for the existence, uniqueness, and global asymptotic stability (GAS) of the equilibrium point for a class of dynamical neural networks. It is shown that the quasi-diagonally column-sum dominant condition on the interconnection matrix of the neural network proves the existence, uniqueness, and GAS of the equilibrium point with respect to all nondecreasing activation functions. This condition is also compared with the previous results derived in the literature.