A new method to optimize the satellite broadcasting schedules using the mean field annealing of a Hopfield neural network

1995 ◽  
Vol 6 (2) ◽  
pp. 470-483 ◽  
Author(s):  
N. Ansari ◽  
E.S.H. Hou ◽  
Y. Yu
Keyword(s):  
Neural Network ◽  
Mean Field ◽  
Hopfield Neural Network ◽  
New Method ◽  
Satellite Broadcasting ◽  
Field Annealing ◽  
The Mean

Application of Kalman Filter and Mean Field Annealing Algorithms in GPS-Based Attitude Determination

1998 ◽  
Vol 51 (1) ◽  
pp. 117-131 ◽  
Author(s):  
Jyh-Ching Juang ◽  
Guo-Shing Huang
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Attitude Determination ◽  
Mean Field ◽  
Hopfield Neural Network ◽  
Integer Ambiguity ◽  
Neural Network Approach ◽  
Field Annealing ◽  
Filter Approach ◽  
Set Up

In this paper, two algorithms of Global Positioning System based attitude determination are proposed. The first algorithm extends the Kalman filter approach to determine the integer ambiguity and the orientation that is needed in a typical gps-based attitude determination problem. The second algorithm explores the mean field annealing neural network approach, which is a combination of the competitive Hopfield neural network and the stochastic simulated annealing technique, to resolve the optimal attitude problems. A test platform is set up for verifying these algorithms. The two algorithms are further compared in terms of computation speed and convergence rate.

Improving Pattern Recognition Rate by Gaussian Hopfield Neural Network

2011 ◽  
Vol 189-193 ◽  
pp. 2042-2045 ◽  
Author(s):  
Shang Jen Chuang ◽  
Chiung Hsing Chen ◽  
Chien Chih Kao ◽  
Fang Tsung Liu
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Gaussian Distribution ◽  
Test Pattern ◽  
Recognition Rate ◽  
Hopfield Neural Network ◽  
Training Data ◽  
New Method ◽  
Gaussian Filter ◽  
Testing Data

English letters cannot be recognized by the Hopfield Neural Network if it contains noise over 50%. This paper proposes a new method to improve recognition rate of the Hopfield Neural Network. To advance it, we add the Gaussian distribution feature to the Hopfield Neural Network. The Gaussian filter was added to eliminate noise and improve Hopfield Neural Network’s recognition rate. We use English letters from ‘A’ to ‘Z’ as training data. The noises from 0% to 100% were generated randomly for testing data. Initially, we use the Gaussian filter to eliminate noise and then to recognize test pattern by Hopfield Neural Network. The results are we found that if letters contain noise between 50% and 53% will become reverse phenomenon or unable recognition [6]. In this paper, we propose to uses multiple filters to improve recognition rate when letters contain noise between 50% and 53%.

OLFACTORY NEURAL NETWORK MODEL

1992 ◽  
Vol 06 (27) ◽  
pp. 1721-1728
Author(s):  
N. KAZAKOVA ◽  
VIK. DOTSENKO
Keyword(s):  
Neural Network ◽  
Phase Diagram ◽  
Mean Field ◽  
Hopfield Neural Network ◽  
Network System ◽  
Zero Temperature ◽  
Field Phase ◽  
Simple Generalization ◽  
Neural Network System ◽  
Olfactory Neural Network

A statistical neural network olfactory model is proposed. The model is a simple generalization of the Hopfield neural network system. Retrieval properties are analysed and the zero temperature mean field phase diagram is obtained.

Identification of Transformer Sympathetic Inrush Based on W-DHNN

2013 ◽  
Vol 441 ◽  
pp. 200-203
Author(s):  
Lan Bing Li ◽  
Mao Fa Gong ◽  
Lei Li ◽  
Jian Yu Zhang ◽  
Hui Ting Ge
Keyword(s):  
Neural Network ◽  
Wavelet Analysis ◽  
Abrupt Change ◽  
Hopfield Neural Network ◽  
New Method ◽  
Fault Current ◽  
Current Signal ◽  
Feature Vectors ◽  
Internal Fault ◽  
Characteristic Values

A new method to identify sympathetic inrush and internal fault current of transformer based on W-DHNN is put forward. Wavelet analysis can detect the abrupt change of the current signal. And extract the feature vectors of the signal. The characteristic values as the input value of discrete Hopfield neural network. Then using discrete Hopfield neural network to discriminate sympathetic inrush and internal fault current. This paper uses PSCAD/EMTDC software to model and emulates different parameters of transformer and fault types. The results show that the method is feasible.

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