A continuous time structure for filtering and prediction using Hopfield Neural Networks

1997 ◽  
pp. 1241-1250 ◽  
Author(s):  
Hector Perez-Meana ◽  
Mariko Nakano-Miyatake
Keyword(s):  
Neural Networks ◽  
Continuous Time ◽  
Hopfield Neural Networks ◽  
Time Structure

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.

scholarly journals Trajectory representation and landmark projection for continuous-time structure from motion

2019 ◽  
Vol 38 (6) ◽  
pp. 686-701 ◽  
Author(s):  
Hannes Ovrén ◽  
Per-Erik Forssén
Keyword(s):  
Structure From Motion ◽  
Continuous Time ◽  
Spline Interpolation ◽  
Time Structure ◽  
Robot Arm ◽  
Good Sense ◽  
Trajectory Representation ◽  
Inertial Measurements ◽  
Derivatives Of ◽  
Fusion Framework

This paper revisits the problem of continuous-time structure from motion, and introduces a number of extensions that improve convergence and efficiency. The formulation with a [Formula: see text]-continuous spline for the trajectory naturally incorporates inertial measurements, as derivatives of the sought trajectory. We analyze the behavior of split spline interpolation on [Formula: see text] and on [Formula: see text], and a joint spline on [Formula: see text], and show that the latter implicitly couples the direction of translation and rotation. Such an assumption can make good sense for a camera mounted on a robot arm, but not for hand-held or body-mounted cameras. Our experiments in the Spline Fusion framework show that a split spline on [Formula: see text] is preferable over an [Formula: see text] spline in all tested cases. Finally, we investigate the problem of landmark reprojection on rolling shutter cameras, and show that the tested reprojection methods give similar quality, whereas their computational load varies by a factor of two.

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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