Scheduling Computation Tasks onto a Multiprocessor System by Mean Field Annealing of a Hopfield Neural Network

1993 ◽  
pp. 163-184 ◽  
Author(s):  
Nirwan Ansari ◽  
Zeeman Z. Zhang ◽  
Edwin S. H. Hou
Keyword(s):  
Neural Network ◽  
Mean Field ◽  
Hopfield Neural Network ◽  
Multiprocessor System ◽  
Field Annealing

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.

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.

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