Exponential stability of continuous-time and discrete-time cellular neural networks with delays
Applied Mathematics and Computation
Global Robust Exponential Stability of Interval Neural Networks with Delays
Neural Processing Letters
Stability analysis of discrete-time recurrent neural networks
IEEE Transactions on Neural Networks
Periodicity of Recurrent Neural Networks with Reaction-Diffusion and Dirichlet Boundary Conditions
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Stability Analysis of a General Class of Continuous-Time Recurrent Neural Networks
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Neural Processing Letters
Global exponential stability of t-s fuzzy neural networks with time-varying delays
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Numerical analysis of a chaotic delay recurrent neural network with four neurons
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
State estimation of markovian jump neural networks with mixed time delays
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
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A general class of discrete-time recurrent neural networks (DTRNNs) is formulated and studied in this paper. Several sufficient conditions are obtained to ensure the global stability of DTRNNs with delays based on induction principle (not based on the well-known Liapunov methods). The obtained results have neither assumed the symmetry of the connection matrix, nor boundedness, monotonicity or the differentiability of the activation functions. In addition, discrete-time analogues of a general class of continuous-time recurrent neural networks (CTRNNs) are derived and studied. The convergence characteristics of CTRNNs are preserved by the discrete-time analogues without any restriction imposed on the uniform discretization step size. Finally, the simulating results demonstrate the validity and feasibility of our proposed approach.