Results 251 to 260 of about 1,813,184 (347)
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IEEE Transactions on Cybernetics, 2018
Xianming Zhang, Q. Han, Z. Zeng
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Xianming Zhang, Q. Han, Z. Zeng
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Stability Analysis for Delayed Neural Networks via a Generalized Reciprocally Convex Inequality
IEEE Transactions on Neural Networks and Learning Systems, 2022This article deals with the stability of neural networks (NNs) with time-varying delay. First, a generalized reciprocally convex inequality (RCI) is presented, providing a tight bound for reciprocally convex combinations.
Huichao Lin +3 more
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Fuzzy Sets Syst., 2020
This paper investigates the issue of the reliable asynchronous sampled-data filtering of Takagi–Sugeno (T–S) fuzzy delayed neural networks with stochastic intermittent faults, randomly occurring time-varying parameters uncertainties and controller gain ...
Kaibo Shi +3 more
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This paper investigates the issue of the reliable asynchronous sampled-data filtering of Takagi–Sugeno (T–S) fuzzy delayed neural networks with stochastic intermittent faults, randomly occurring time-varying parameters uncertainties and controller gain ...
Kaibo Shi +3 more
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Fuzzy Sets Syst., 2020
This paper deals with the non-fragile memory filtering issue of T-S fuzzy delayed neural networks with randomly occurring time-varying parameters uncertainties and variable sampling rates.
Kaibo Shi +4 more
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This paper deals with the non-fragile memory filtering issue of T-S fuzzy delayed neural networks with randomly occurring time-varying parameters uncertainties and variable sampling rates.
Kaibo Shi +4 more
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Neural Networks, 2020
The actuator of any physical control systems is constrained by amplitude and energy, which causes the control systems to be inevitably affected by actuator saturation.
Deqiang Ouyang +4 more
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The actuator of any physical control systems is constrained by amplitude and energy, which causes the control systems to be inevitably affected by actuator saturation.
Deqiang Ouyang +4 more
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Neural Networks, 2020
In this paper, the finite-time resilient H∞ state estimation problem is investigated for a class of discrete-time delayed neural networks. For the sake of energy saving, a dynamic event-triggered mechanism is employed in the design of state estimator for
Yufei Liu, Bo Shen, Huisheng Shu
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In this paper, the finite-time resilient H∞ state estimation problem is investigated for a class of discrete-time delayed neural networks. For the sake of energy saving, a dynamic event-triggered mechanism is employed in the design of state estimator for
Yufei Liu, Bo Shen, Huisheng Shu
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FAST TIME DELAY NEURAL NETWORKS
International Journal of Neural Systems, 2005This paper presents a new approach to speed up the operation of time delay neural networks. The entire data are collected together in a long vector and then tested as a one input pattern. The proposed fast time delay neural networks (FTDNNs) use cross correlation in the frequency domain between the tested data and the input weights of neural networks.
Hazem M, El-Bakry, Qiangfu, Zhao
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Synchronization of Delayed Neural Networks via Integral-Based Event-Triggered Scheme
IEEE Transactions on Neural Networks and Learning Systems, 2020This article investigates the event-triggered synchronization of delayed neural networks (NNs). A novel integral-based event-triggered scheme (IETS) is proposed where the integral of the system states, and past triggered data over a period of time are ...
Liruo Zhang +3 more
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IEEE Transactions on Neural Networks and Learning Systems, 2020
In this paper, the impulsive effects on projective synchronization between the parameter mismatched neural networks with mixed time-varying delays have been analyzed.
Rakesh Kumar +3 more
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In this paper, the impulsive effects on projective synchronization between the parameter mismatched neural networks with mixed time-varying delays have been analyzed.
Rakesh Kumar +3 more
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Europhysics Letters (EPL), 1988
In this letter we consider the effect of random transmission delays on the dynamics of a fully connected neural network. We show that, if these delays are also present during a learning stage in which patterns are presented in succession, the network will be capable of regenerating this sequence of patterns.
A. C. C Coolen, C. C. A. M Gielen
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In this letter we consider the effect of random transmission delays on the dynamics of a fully connected neural network. We show that, if these delays are also present during a learning stage in which patterns are presented in succession, the network will be capable of regenerating this sequence of patterns.
A. C. C Coolen, C. C. A. M Gielen
openaire +1 more source