Reetrant spin glass behaviour in the replica symmetric solution of the Hopfield neural network model

1992 ◽  
Vol 2 (3) ◽  
pp. 247-250 ◽  
Author(s):  
Jean-Pierre Naef ◽  
Andrew Canning
Keyword(s):  
Neural Network ◽  
Spin Glass ◽  
Network Model ◽  
Neural Network Model ◽  
Symmetric Solution ◽  
Hopfield Neural Network ◽  
Spin Glass Behaviour

THE INVESTIGATION OF THE SPIN GLASS PROPERTIES OF THE HOPFIELD NEURAL NETWORK MODEL

2005 ◽  
Vol 16 (03) ◽  
pp. 427-437 ◽  
Author(s):  
FATİH YAŞAR
Keyword(s):  
Neural Network ◽  
Spin Glass ◽  
Network Model ◽  
Neural Network Model ◽  
Mean Field ◽  
Hopfield Neural Network ◽  
Infinite Range Interactions ◽  
Physical Quantities ◽  
Infinite Range ◽  
Good Agreement

In this work, the Hopfield neural network model with infinite-range interactions is simulated by using the multicanonical algorithm. All simulations and measurements are done in spin glass states of the model with discrete ± 1 values of the random variables. Physical quantities such as the energy density, the ground-state entropy and the order parameters are evaluated at all temperatures. Our results in the spin glass region show multiple degenerate ground-states and good agreement with the replica symmetry mean field solutions.

scholarly journals Contribution to image restoration using a neural network model

2002 ◽  
Vol Volume 1, 2002 ◽  
Author(s):  
Karim Achour ◽  
Nadia Zenati ◽  
Oualid Djekoune
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
Network Model ◽  
Neural Network Model ◽  
Hopfield Neural Network ◽  
Optimization Method ◽  
Nous Proposons ◽  
International Audience ◽  
High Level

International audience The reduction of the blur and the noise is an important task in image processing. Indeed, these two types of degradation are some undesirable components during some high level treatments. In this paper, we propose an optimization method based on neural network model for the regularized image restoration. We used in this application a modified Hopfield neural network. We propose two algorithms using the modified Hopfield neural network with two updating modes : the algorithm with a sequential updates and the algorithm with the n-simultaneous updates. The quality of the obtained result attests the efficiency of the proposed method when applied on several images degraded with blur and noise. La réduction du bruit et du flou est une tâche très importante en traitement d'images. En effet, ces deux types de dégradations sont des composantes indésirables lors des traitements de haut niveau. Dans cet article, nous proposons une méthode d'optimisation basée sur les réseaux de neurones pour résoudre le problème de restauration d'images floues-bruitées. Le réseau de neurones utilisé est le réseau de « Hopfield ». Nous proposons deux algorithmes utilisant deux modes de mise à jour: Un algorithme avec un mode de mise à jour séquentiel et un algorithme avec un mode de mise à jour n-simultanée. L'efficacité de la méthode mise en œuvre a été testée sur divers types d'images dégradées.

scholarly journals Intelligent Design Based Neural Network Model for Measuring Analysis of the College Teachers’ Teaching Ability

2020 ◽  
Vol 15 (15) ◽  
pp. 176
Author(s):  
Yihui Chen ◽  
Mingli Yang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Intelligent Design ◽  
Core Competencies ◽  
Hopfield Neural Network ◽  
College Teachers ◽  
Measurement And Analysis ◽  
Teaching Ability ◽  
Measure Analysis

The teaching ability of College Teachers is regarded as one of the core competencies and a critical indicator for measuring comprehensive strength for a college. However, its evaluation process is a highly complex system decision-making, for there are various factors that influence on the assessment of for the College Teachers’ the teaching ability. The traditional methods have drawbacks of strong subjectivity, so they are difficult to correctly evaluate the teaching ability of College Teachers, resulting in decrease of measurement accuracy. Based on the analysis of the relevant factors, this paper presents an intelligent design based neural network model of discrete Hopfield for the measurement and analysis of College Teachers' teaching ability. Firstly, a Hopfield neural network model for the measure analysis of the teaching ability is established, and eleven measure analysis indexes are selected as input information of the Hopfield neural network model. Secondly, the College Teachers' teaching ability grades are chosen as the model output, then the input and output model based on the relationship among the self-learning abilities of neural network is established. Finally, the simulation experiment is obtained by using MATLAB. The simulation results show that the model has the characteristics of high efficiency, objectivity and fairness, which can meet the requirements of the measurement and analysis of College Teachers' teaching ability.

A quantum Hopfield neural network model and image recognition

2020 ◽  
Vol 17 (4) ◽  
pp. 045201 ◽  
Author(s):  
Ge Liu ◽  
Wen-Ping Ma ◽  
Hao Cao ◽  
Liang-Dong Lyu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Image Recognition ◽  
Neural Network Model ◽  
Hopfield Neural Network
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