Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Delay-dependent H∞ and generalized H2 filtering for delayed neural networks
IEEE Transactions on Circuits and Systems Part I: Regular Papers
New Lyapunov-Krasovskii functionals for global asymptotic stability of delayed neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Stochastic Exponential Stability for Markovian Jumping BAM Neural Networks With Time-Varying Delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Impulsive Effects on Stability of Fuzzy Cohen–Grossberg Neural Networks With Time-Varying Delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
State estimation for delayed neural networks
IEEE Transactions on Neural Networks
Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays
IEEE Transactions on Neural Networks
Delay-dependent state estimation for delayed neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
New Delay-Dependent Stability Criteria for Neural Networks With Time-Varying Delay
IEEE Transactions on Neural Networks
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
H∞ filtering of markovian jumping neural networks with time delays
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
pth Moment Exponential Stability of Stochastic Recurrent Neural Networks with Markovian Switching
Neural Processing Letters
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The state estimation problem for discrete neural networks with Markovian jumping parameters and time-varying delays is investigated. The considered transition probabilities of the mode jumps are assumed to be partially unknown. The purpose of the state estimation problem is to design a state estimator to estimate the neuron states ensuring the dynamics of the estimation error stochastically stable. In terms of a novel Lyapunov functional, the delay-dependent sufficient conditions for the existence of desired state estimator are derived. A numerical example is given to show the validness of the established results.