Global asymptotic stability of discrete-time cellular neural networks
Sabri Arik, A. Kilinc, F. Acar Savacı · 2002
This paper presents two sufficient conditions for global stability of discrete-time cellular neural networks (DTCNNs). It is shown that if the first or second norm of the feedback matrix is smaller than one, then a DTCNN converges to a unique and globally asymptotically stable equilibrium point for every external input.