Results 271 to 280 of about 131,048 (296)
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Communications in Nonlinear Science and Numerical Simulation, 2023
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Fengyi Liu, Yongqing Yang, Qi Chang
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Fengyi Liu, Yongqing Yang, Qi Chang
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Exponential convergence for HRNNs with continuously distributed delays in the leakage terms
Neural Computing and Applications, 2012This paper considers exponential convergence for a class of high-order recurrent neural networks (HRNNs) with continuously distributed delays in the leakage terms (i.e., “leakage delays”). Without assuming the boundedness on the activation functions, some sufficient conditions are derived to ensure that all solutions of this system converge ...
Zhibin Chen, Mingquan Yang
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A new delay distribution model to take long-term degradation into account
2013 IEEE 4th Latin American Symposium on Circuits and Systems (LASCAS), 2013The long-term degradation due to aging such as NBTI (Negative Bias Temperature Instability) is a hot issue in the current circuit design using nanometer process technologies, since it causes a delay fault in the field. In order to resolve the problem, we must estimate delay variation caused by long-term degradation in design stage, but over estimation ...
Shuji Tsukiyama +2 more
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The long-term distribution of differential group delay in a recirculating loop
Technical Digest: Symposium on Optical Fiber Measurements, 2004., 2004It is well known that the distribution of differential group delay (DGD) in a straight-line optical fiber transmission system is a Maxwellian when the fiber realization drifts ergodically and the fiber is statistically homogeneous [I].
null Hai Xu +5 more
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Neural Processing Letters, 2015
In this paper, a reaction-diffusion neural network with time delay in leakage terms and distributed synaptic transmission delays under homogeneous Neumann boundary conditions is investigated. By analyzing the corresponding characteristic equation, the local stability of the trivial uniform steady state and the existence of Hopf bifurcation are ...
Xiaohong Tian, Rui Xu 0003
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In this paper, a reaction-diffusion neural network with time delay in leakage terms and distributed synaptic transmission delays under homogeneous Neumann boundary conditions is investigated. By analyzing the corresponding characteristic equation, the local stability of the trivial uniform steady state and the existence of Hopf bifurcation are ...
Xiaohong Tian, Rui Xu 0003
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Neurocomputing, 2010
In this paper, the boundedness and exponential stability for nonautonomous fuzzy cellular neural networks (FCNNs) with distributed delays and reaction-diffusion terms are investigated. By constructing suitable Lyapunov functional, introducing ingeniously some real parameters and applying the elementary inequality, we establish a series of criteria on ...
Shuyun Niu, Haijun Jiang, Zhidong Teng
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In this paper, the boundedness and exponential stability for nonautonomous fuzzy cellular neural networks (FCNNs) with distributed delays and reaction-diffusion terms are investigated. By constructing suitable Lyapunov functional, introducing ingeniously some real parameters and applying the elementary inequality, we establish a series of criteria on ...
Shuyun Niu, Haijun Jiang, Zhidong Teng
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P Balasubramaniam +2 more
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Nonlinear Analysis: Real World Applications, 2007
A system of two nonlinear reaction-diffusion equations, including distributed time delays and time-periodic forcing, is studied. The system is motivated as a model for an associative memory neural network. It is proved that, under appropriate conditions, the system has a unique periodic solution (with the same period as that of the forcing), which is ...
Song, Qiankun, Cao, Jinde
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A system of two nonlinear reaction-diffusion equations, including distributed time delays and time-periodic forcing, is studied. The system is motivated as a model for an associative memory neural network. It is proved that, under appropriate conditions, the system has a unique periodic solution (with the same period as that of the forcing), which is ...
Song, Qiankun, Cao, Jinde
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Neural Processing Letters, 2013
This paper concerns with exponential convergence for a class of high-order recurrent neural networks with continuously distributed delays in the leakage terms. Without assuming the boundedness on the activation functions, some sufficient conditions are derived to ensure that all solutions of the networks converge exponentially to the zero point by ...
Yuehua Yu, Weidong Jiao
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This paper concerns with exponential convergence for a class of high-order recurrent neural networks with continuously distributed delays in the leakage terms. Without assuming the boundedness on the activation functions, some sufficient conditions are derived to ensure that all solutions of the networks converge exponentially to the zero point by ...
Yuehua Yu, Weidong Jiao
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Rendiconti del Circolo Matematico di Palermo Series 2, 2022
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Latifa Moumen, Salah-Eddine Rebiai
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Latifa Moumen, Salah-Eddine Rebiai
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