Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Novel robust stability criteria for stochastic hopfield neural networks with time delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
Novel weighting-delay-based stability criteria for recurrent neural networks with time-varying delay
IEEE Transactions on Neural Networks
Robust stability analysis for stochastic neural networks with time-varying delay
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Stability analysis for discrete-time Markovian jump neural networks with mixed time-delays
Expert Systems with Applications: An International Journal
Stochastic Exponential Stability for Markovian Jumping BAM Neural Networks With Time-Varying Delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Exponential Stability Analysis for Neural Networks With Time-Varying Delay
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Novel Exponential Stability Criteria of High-Order Neural Networks With Time-Varying Delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Markovian architectural bias of recurrent neural networks
IEEE Transactions on Neural Networks
Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Global Robust Stability Criteria for Interval Delayed Full-Range Cellular Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Delay-Dependent Stability Analysis for Switched Neural Networks With Time-Varying Delay
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper is concerned with the global exponential estimating problem of delayed stochastic neural networks with Markovian switching. By fully taking the inherent characteristic of such kinds of neural networks into account, a novel stochastic Lyapunov functional is constructed in which as many as possible of the positive definite matrices are dependent on the system mode and a triple-integral term is introduced. Based on it, a delay- and mode-dependent criterion is derived under which not only the neural network is mean square exponentially stable but also the decay rate is well obtained. Moreover, it is shown that the established stability condition includes some existing ones as its special cases, and is thus less conservative. This approach is then extended to two more general cases where mode-dependent time-varying delays and parameter uncertainties are considered. Finally, three numerical examples are presented to demonstrate the performance and effectiveness of the developed approach.