Exponential stability of continuous-time and discrete-time cellular neural networks with delays
Applied Mathematics and Computation
Exponential Periodicity of Continuous-time and Discrete-Time Neural Networks with Delays
Neural Processing Letters
Robust H/sub /spl infin// filtering for stochastic time-delay systems with missing measurements
IEEE Transactions on Signal Processing
Recurrent neural nets as dynamical Boolean systems with application to associative memory
IEEE Transactions on Neural Networks
Hopfield network with constraint parameter adaptation for overlapped shape recognition
IEEE Transactions on Neural Networks
Recurrent neural network as a linear attractor for pattern association
IEEE Transactions on Neural Networks
Gradient calculations for dynamic recurrent neural networks: a survey
IEEE Transactions on Neural Networks
Delay-distribution-dependent robust H∞control for discrete-time systems with stochastic delays
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Stochastic stability and bifurcation analysis on hopfield neural networks with noise
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
Stochastic stability and bifurcation analysis on Hopfield neural networks with noise
Expert Systems with Applications: An International Journal
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
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A robust delay-distribution-dependent stochastic stability analysis is conducted for a class of discrete-time stochastic delayed neural networks (DSNNs) with parameter uncertainties. The effects of both variation range and distribution probability of the time delay are taken into account in the proposed approach. The distribution probability of time delay is translated into parameter matrices of the transferred DSNNs model, in which the parameter uncertainties are norm-bounded, the stochastic disturbances are described in term of a Brownian motion, and the time-varying delay is characterized by introducing a Bernoulli stochastic variable. Some delay-distribution-dependent criteria for the DSNNs to be robustly globally exponentially stable in the mean square sense are achieved by Lyapunov method and introducing some new analysis techniques. Two numerical examples are provided to show the effectiveness and applicability of the proposed method.