Power law decay of stored pattern stability in sparse Hopfield neural networks

2021 ◽  
Vol 73 (2) ◽  
pp. 025601
Author(s):  
Fei Fang ◽  
Zhou Yang ◽  
Sheng-Jun Wang
Keyword(s):  
Neural Networks ◽  
Power Law ◽  
Hopfield Neural Networks ◽  
Power Law Decay ◽  
Pattern Stability

scholarly journals Inequalities and pth moment exponential stability of impulsive delayed Hopfield neural networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yutian Zhang ◽  
Guici Chen ◽  
Qi Luo
Keyword(s):  
Neural Networks ◽  
Lyapunov Function ◽  
Exponential Stability ◽  
Hopfield Neural Networks ◽  
New Method ◽  
Integral Inequalities ◽  
Delay Function ◽  
Pth Moment Exponential Stability ◽  
Moment Exponential Stability

AbstractIn this paper, the pth moment exponential stability for a class of impulsive delayed Hopfield neural networks is investigated. Some concise algebraic criteria are provided by a new method concerned with impulsive integral inequalities. Our discussion neither requires a complicated Lyapunov function nor the differentiability of the delay function. In addition, we also summarize a new result on the exponential stability of a class of impulsive integral inequalities. Finally, one example is given to illustrate the effectiveness of the obtained results.

Nonlinear Resonance in Neuron Dynamics

1995 ◽  
Vol 50 (8) ◽  
pp. 718-726 ◽  
Author(s):  
Scott Rader ◽  
Diek W. Wheeler ◽  
W.C. Schieve ◽  
Pranab Das
Keyword(s):  
Neural Networks ◽  
Energy Transfer ◽  
Power Spectrum ◽  
Nonlinear Resonance ◽  
Nonlinear Oscillators ◽  
Hopfield Neural Networks ◽  
Neuron Dynamics

Abstract Hübler's technique using aperiodic forces to drive nonlinear oscillators to resonance is analyzed. The oscillators being examined are effective neurons that model Hopfield neural networks. The method is shown to be valid under several different circumstances. It is verified through analysis of the power spectrum, force, resonance, and energy transfer of the system.

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