Multi-variable Volterra kernels identification using time-delay neural networks: application to unsteady aerodynamic loading

2019 ◽  
Vol 97 (1) ◽  
pp. 767-780 ◽  
Author(s):  
N. C. G. de Paula ◽  
F. D. Marques
AIAA Journal ◽  
2019 ◽  
Vol 57 (4) ◽  
pp. 1725-1735 ◽  
Author(s):  
Natália C. G. de Paula ◽  
Flávio D. Marques ◽  
Walter A. Silva

2007 ◽  
Vol 22 (2) ◽  
pp. 229-241 ◽  
Author(s):  
Mohammad Karamouz ◽  
Saman Razavi ◽  
Shahab Araghinejad

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Guiying Chen ◽  
Linshan Wang

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.


2011 ◽  
Vol 20 (04) ◽  
pp. 657-666
Author(s):  
CHOON KI AHN

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.


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