Improving the image recognition capability of Hopfield neural networks

2002 ◽  
Author(s):  
M.C. Humphrey ◽  
G. Holmes ◽  
S.J. Cunningham
Keyword(s):  
Neural Networks ◽  
Image Recognition ◽  
Hopfield Neural Networks ◽  
Recognition Capability

scholarly journals Some properties of asymmetric Hopfield neural networks with finite time of transition between states

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Ibragim Suleimenov ◽  
Grigoriy Mun ◽  
Sergey Panchenko ◽  
Ivan Pak
Keyword(s):  
Neural Networks ◽  
Image Recognition ◽  
Finite Time ◽  
Output Signal ◽  
The State ◽  
Hopfield Neural Networks ◽  
Ternary Logic ◽  
Logical Variable ◽  
Recognition Procedure ◽  
Oscillation Modes

AbstractThere were implemented samples of asymmetric Hopfield neural networks which have finite time of transition from one state to another. It was shown that in such systems, various oscillation modes could occur. It was revealed that the oscillation of the output signal of certain neuron could be treated as extra logical variable, which describes the state of the neuron. Asymmetric Hopfield neural networks are described in terms of ternary logic. Such logic may be employed in image recognition procedure.

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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