Utilizing Hopfield Neural Network And An Improved Simulated Annealing Procedure For Design Optimization Of Electromagnetic Devices

1993 ◽  
Author(s):  
O.A. Mohammed ◽  
F.G. Uler
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Design Optimization ◽  
Hopfield Neural Network ◽  
Electromagnetic Devices ◽  
Annealing Procedure ◽  
Simulated Annealing Procedure

scholarly journals Research on Material Allocation Path Based on Hopfield Neural Network and Simulated Annealing Hybrid Algorithm

2020 ◽  
Vol 1682 ◽  
pp. 012003
Author(s):  
Junpeng Xi ◽  
Xudong Zhao ◽  
Yingshuang Zhu ◽  
Baoqiang Xiao ◽  
Shun Chen
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Hybrid Algorithm ◽  
Hopfield Neural Network

Molecular dynamics simulations reveal how graphene oxide stabilizes and activates lipase in an anhydrous gas

2019 ◽  
Vol 21 (45) ◽  
pp. 25425-25430 ◽  
Author(s):  
Zhongwang Fu ◽  
Weina Xu ◽  
Gong Chen ◽  
Zheyu Wang ◽  
Diannan Lu ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Graphene Oxide ◽  
Simulated Annealing ◽  
Molecular Dynamics Simulations ◽  
Md Simulations ◽  
Candida Antarctica ◽  
Lipase B ◽  
Annealing Procedure ◽  
Dynamics Simulations ◽  
Simulated Annealing Procedure

The interaction between Candida antarctica lipase B (CALB) and graphene oxide (GO) in an anhydrous gas was studied using molecular dynamics (MD) simulations augmented with a simulated annealing procedure to accelerate relaxation towards equilibrium.

scholarly journals Identification of Shaft Centerline Orbit for Wind Power Units Based on Hopfield Neural Network Improved by Simulated Annealing

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Kun Ren ◽  
Jihong Qu
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Wind Power ◽  
Hopfield Neural Network ◽  
Affine Invariant ◽  
Characteristic Parameters ◽  
Invariant Moments ◽  
The Status ◽  
Identification Model ◽  
Better Than

In the maintenance system of wind power units, shaft centerline orbit is an important feature to diagnosis the status of the unit. This paper presents the diagnosis of the orbit as follows: acquire characters of orbit by the affine invariant moments, take this as the characteristic parameters of neural networks to construct the identification model, utilize Simulated Annealing (SA) Algorithm to optimize the weights matrix of Hopfield neural network, and then some typical faults were selected as examples to identify. Experiment’s results show that SA-Hopfield identification model performed better than the previous methods.

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