Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Qualitative Analysis and Synthesis of Recurrent Neural Networks
Qualitative Analysis and Synthesis of Recurrent Neural Networks
Automatica (Journal of IFAC)
A survey of linear matrix inequality techniques in stability analysis of delay systems
International Journal of Systems Science
Technical communique: Absolute stability of time-delay systems with sector-bounded nonlinearity
Automatica (Journal of IFAC)
State estimation for delayed neural networks
IEEE Transactions on Neural Networks
Delay-dependent state estimation for delayed neural networks
IEEE Transactions on Neural Networks
Stability Analysis for Neural Networks With Time-Varying Interval 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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This paper investigates the problem of state estimation for Markovian jump Hopfield neural networks (MJHNNs) with discrete and distributed delays. The MJHNN model, whose neuron activation function and nonlinear perturbation of the measurement equation satisfy sector-bounded conditions, is first considered and it is more general than those models studied in the literature. An estimator that guarantees the mean-square exponential stability of the corresponding error state system is designed. Moreover, a mean-square exponential stability condition for MJHNNs with delays is presented. The results are dependent upon both discrete and distributed delays. More importantly, all of the model transformations, cross-terms bounding techniques and free additional matrix variables are avoided in the derivation, so the results obtained have less conservatism and simpler formulations than the existing ones. Numerical examples are given which demonstrate the validity of the theoretical results.