Passive learning and input-to-state stability of switched Hopfield neural networks with time-delay

2010 ◽  
Vol 180 (23) ◽  
pp. 4582-4594 ◽  
Author(s):  
Choon Ki Ahn
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Hopfield Neural Networks ◽  
Passive Learning

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.

Stability for Hopfield Neural Networks With Time Delay

2002 ◽  
Vol 8 (1) ◽  
pp. 13-18 ◽  
Author(s):  
Linshan Wang ◽  
Daoyi Xu
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Asymptotic Stability ◽  
Equilibrium Point ◽  
Global Asymptotic Stability ◽  
Sufficient Conditions ◽  
Hopfield Neural Networks ◽  
Unique Equilibrium ◽  
Transmission Delays

In this paper, the global asymptotic stability of the equilibrium point of Hopfield neural networks with interneuronal transmission delays is studied. Some sufficient conditions related to the existence of a unique equilibrium point and its global asymptotic stability are derived.

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