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 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

scholarly journals Progressive optimization methods for applied in computer network

2009 ◽  
Vol 57 (6) ◽  
pp. 45-50
Author(s):  
Miroslav Cepl ◽  
Jiří Šťastný
Keyword(s):  
Neural Network ◽  
Computer Network ◽  
Hopfield Neural Network ◽  
Optimization Methods ◽  
Processing Unit ◽  
Communications Networks ◽  
Active Elements ◽  
Artificial Neural ◽  
And Function ◽  
Network Switch

Standard core of communications’ networks is represent by active elements, which carries out the processing of transmitted data units. Based on the results of the processing the data are transmitted from sender to recipient. The hardest challenge of the active elements present to determine what the data processing unit and what time of the system to match the processing priority assigned to individual data units. Based on the analysis of the architecture and function of active network components and algorithms, artificial neural networks can be assumed to be effectively useable to manage network elements. This article focuses on the design and use of the selected type of artificial neural network (Hopfield neural network) for the optimal management of network switch.

Neural network-based optimization model for sprint data collection

2021 ◽  
pp. 1-11
Author(s):  
Guangfei Luo
Keyword(s):  
Neural Network ◽  
Data Collection ◽  
Data Acquisition ◽  
Bp Neural Network ◽  
Optimization Model ◽  
Balance Control ◽  
Performance Parameters ◽  
Advantages And Disadvantages ◽  
Network Algorithm ◽  
Neural Network Algorithm

Sprint data has the characteristics of quality and continuity, but due to the limitations of optimization algorithm, the existing sprint data acquisition optimization model has the problem of low optimization performance parameters. Therefore, a data acquisition control optimization model based on neural network is proposed. This paper analyzes the advantages and disadvantages of neural network algorithm, combined with the sprint data collection optimization requirements, introduces BP neural network algorithm, based on this, uses multiple sensors, based on baud interval balance control to collect sprint data, applies BP neural network algorithm to compress, integrate and classify sprint data, realizes the sprint data collection and optimization. The experimental results show that the optimization performance parameters of the model are large, which fully shows that the model has good data acquisition optimization performance.

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