Abstract-In this paper, an analysis of the absolute stability for a general class of discrete-time recurrent neural networks (RNN's) is presented. A discrete-time model of RNN's is represented by a set of nonlinear difference equations. Some sufficient conditions for the absolute stability are derived using Ostrowski's theorem and the similarity transformation approach. For a given RNN model, these conditions are determined by the synaptic weight matrix of the network. The results reported in this paper need fewer constraints on the weight matrix and the model than in previously published studies.
For 1-layer RNNs, REF give sufficient conditions for stability in terms of the norm W and the Lipschitz constant of the non-linearity.
Absolute stability conditions for discrete-time recurrent neural networks
IEEE transactions on neural networks
IEEE transactions on neural networks
Mathematics Medicine Computer Science