Robustness analysis of Hopfield and modified Hopfield neural networks in time domain

2002 ◽  
Author(s):  
Jie Shen ◽  
S.N. Balakrishnan
Keyword(s):  
Neural Networks ◽  
Time Domain ◽  
Robustness Analysis ◽  
Hopfield Neural Networks

CHAOS, BIFURCATION AND ROBUSTNESS OF A CLASS OF HOPFIELD NEURAL NETWORKS

2011 ◽  
Vol 21 (03) ◽  
pp. 885-895 ◽  
Author(s):  
WEN-ZHI HUANG ◽  
YAN HUANG
Keyword(s):  
Neural Networks ◽  
Dynamical Systems ◽  
Chaotic Behavior ◽  
Robustness Analysis ◽  
Original System ◽  
Hopfield Neural Networks ◽  
Phase Portraits ◽  
Chaotic Attractors ◽  
Computer Assisted ◽  
New Class

Chaos, bifurcation and robustness of a new class of Hopfield neural networks are investigated. Numerical simulations show that the simple Hopfield neural networks can display chaotic attractors and limit cycles for different parameters. The Lyapunov exponents are calculated, the bifurcation plot and several important phase portraits are presented as well. By virtue of horseshoes theory in dynamical systems, rigorous computer-assisted verifications for chaotic behavior of the system with certain parameters are given, and here also presents a discussion on the robustness of the original system. Besides this, quantitative descriptions of the complexity of these systems are also given, and a robustness analysis of the system is presented too.

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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