Solubility and stability of recurrent neural networks with nonlinearity or time-varying delays

Because to apply a deterministic RNN to a noisy time series and the existence of a linear approximation are doubtful, we reconsider the solubility and stability of a recurrent neural network (RNN). Simpler methods are proposed to replace the complicated nonsingular M-matrix method when nonlinearities exist and to replace the complicated linear matrix inequality method when time-varying delays exist.