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. (C) 2010 Elsevier B.V. All rights reserved.
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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
