Application Based on PSO of Hopfield Neural Network Algorithm in Dynamic VRPSPD

2015 ◽  
Author(s):  
Liu Lan ◽  
Wang Xia
Keyword(s):  
Neural Network ◽  
Hopfield Neural Network ◽  
Network Algorithm ◽  
Neural Network Algorithm

scholarly journals A hopfield neural network algorithm for automatic name placement for point feature

2004 ◽  
Vol 7 (2) ◽  
pp. 144-147 ◽  
Author(s):  
Fan Hong ◽  
Tang Guoqiang ◽  
Zhang Zuxun ◽  
Du Daosheng
Keyword(s):  
Neural Network ◽  
Hopfield Neural Network ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Point Feature

The Computer Network Optimization Model Based on Neural Network Algorithm Research

2013 ◽  
Vol 798-799 ◽  
pp. 545-548
Author(s):  
Xun Wang ◽  
Jie Rong
Keyword(s):  
Neural Network ◽  
Network Optimization ◽  
Optimization Model ◽  
Computer Network ◽  
Hopfield Neural Network ◽  
Optimization Methods ◽  
Information Storage ◽  
Distributed Information ◽  
Network Algorithm ◽  
Neural Network Algorithm

The speed of development of the computer network is an urgent need to comprehensively improve and optimize the overall performance of the network. Neural network algorithm has a massively parallel processing and distributed information storage, Hopfield neural network showed a unique advantage in the associative memory and optimization based on the neural network algorithm for computer network optimization model of Hopfield neural network theory and reality computer network, modern optimization methods, it is combined.

scholarly journals Sea Surface Current Velocity Retrieving from TanDAM-X Satellite Data

2021 ◽  
pp. 1-10
Author(s):  
M. Marghany ◽  
J.L. Genderen
Keyword(s):  
Neural Network ◽  
Coastal Waters ◽  
Optimal Solution ◽  
Surface Current ◽  
Hopfield Neural Network ◽  
Pareto Optimal Solution ◽  
Sea Surface ◽  
Pareto Optimal ◽  
Network Algorithm ◽  
Neural Network Algorithm

This is the first investigation for the use of TanDEM-X data, satellite for the Malaysian coastal waters. This aims at utilizing an optimization of the Hopfield neural network to retrieve variation of sea surface current along Malaysian coastal waters. In doing so, a multi-objective evolutionary algorithm based on the Pareto front is used to minimize the error produced due to non-linearity between TanDEM-X data and sea surface movements. This work aimed at retrieving sea surface current from TanDEM-X data along the coastal waters of Malaysia. Two approaches have been implemented, the Hopfield neural network algorithm and Pareto optimal solution. The study shows that the Pareto optimal solution has a higher performance than the Hopfield neural network algorithm with a lower RMSE of ±0.009. Furthermore, a Pareto optimal solution can determine the sea surface current pattern variation along the coastal water from TanDEM-X data. In conclusion, TanDEM-X data shows an excellent promise for retrieving sea surface currents.

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