On memristor-based impulsive neural networks with time-delay

2017 ◽  
Author(s):  
Bin Hu ◽  
Zhi-Hong Guan ◽  
Zhi-Wei Liu ◽  
Xiao-Wei Jiang
Keyword(s):  
Neural Networks ◽  
Time Delay

Long-lead seasonal rainfall forecasting using time-delay recurrent neural networks: a case study

2007 ◽  
Vol 22 (2) ◽  
pp. 229-241 ◽  
Author(s):  
Mohammad Karamouz ◽  
Saman Razavi ◽  
Shahab Araghinejad
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Recurrent Neural Networks ◽  
Seasonal Rainfall ◽  
Rainfall Forecasting

scholarly journals Global Exponential Robust Stability of Static Interval Neural Networks with Time Delay in the Leakage Term

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Guiying Chen ◽  
Linshan Wang
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Robust Stability ◽  
Sufficient Conditions ◽  
Leakage Term ◽  
Interval Neural Networks ◽  
Global Exponential Robust Stability ◽  
The Stability ◽  
Delay Differential ◽  
Delay Differential Inequality

The stability of a class of static interval neural networks with time delay in the leakage term is investigated. By using the method ofM-matrix and the technique of delay differential inequality, we obtain some sufficient conditions ensuring the global exponential robust stability of the networks. The results in this paper extend the corresponding conclusions without leakage delay. An example is given to illustrate the effectiveness of the obtained results.

DESIGN OF STATE ESTIMATOR FOR SWITCHED HOPFIELD NEURAL NETWORKS WITH TIME-DELAY

2011 ◽  
Vol 20 (04) ◽  
pp. 657-666
Author(s):  
CHOON KI AHN
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Estimation Error ◽  
Hopfield Neural Networks ◽  
Linear Matrix ◽  
Asymptotically Stable ◽  
State Estimator ◽  
Error System ◽  
Delay Dependent ◽  
Dependent State

In this paper, the delay-dependent state estimation problem for switched Hopfield neural networks with time-delay is investigated. Based on the Lyapunov–Krasovskii stability theory, a new delay-dependent state estimator for switched Hopfield neural networks is established to estimate the neuron states through available output measurements such that the estimation error system is asymptotically stable. The gain matrix of the proposed estimator is characterized in terms of the solution to a linear matrix inequality (LMI), which can be checked readily by using some standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed state estimator.

Dissipativity and stability analysis of fractional-order complex-valued neural networks with time delay

2017 ◽  
Vol 86 ◽  
pp. 42-53 ◽  
Author(s):  
G. Velmurugan ◽  
R. Rakkiyappan ◽  
V. Vembarasan ◽  
Jinde Cao ◽  
Ahmed Alsaedi
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Time Delay ◽  
Fractional Order ◽  
Order Complex ◽  
Complex Valued
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