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 Logistics Distribution Route Optimization Model Based on Recursive Fuzzy Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Binbin Liu
Keyword(s):  
Neural Network ◽  
Optimization Model ◽  
Fuzzy Neural Network ◽  
Hot Spot ◽  
Path Optimization ◽  
Logistics Distribution ◽  
Model Based ◽  
Network Algorithm ◽  
Fuzzy Neural ◽  
Neural Network Algorithm

In recent years, more and more attention has been paid to the utilization of data and information in the logistics distribution path optimization system of e-commerce, but it is difficult to have scientific guarantee in the process of determining the optimal distribution path scheme of e-commerce. How to realize the optimization and adaptive setting of distribution path by using intelligent algorithm has become a hot spot. To battle these issues, this paper studies the logistics distribution path optimization model based on recursive fuzzy neural network algorithm. This paper analyses the research status of logistics distribution path determination scheme and applies the recursive fuzzy neural network algorithm in the selection of e-commerce logistics distribution path scheme. The experimental results show that the recursive fuzzy neural network algorithm can realize the optimization of e-commerce logistics distribution path, and the best distribution route can be made according to the characteristic difference of logistics distribution route, and its distribution accuracy can reach more than 97%.

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

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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