An Empirical Study on Causality between Financial Development and Economic Growth of Fujian Province

2011 ◽  
Author(s):  
Lifang Zhang ◽  
Fujin Zhang
Keyword(s):  
Economic Growth ◽  
Empirical Study ◽  
Financial Development ◽  
Fujian Province

scholarly journals Empirical Study of the Relationship between Financial Development and Economic Growth Based on Intelligent Algorithms Based on Wireless Network Communication

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Juan Xie ◽  
Yunfei Cao
Keyword(s):  
Economic Growth ◽  
Empirical Study ◽  
Wireless Network ◽  
Financial Development ◽  
Network Communication ◽  
Feasible Region ◽  
Intelligent Algorithms ◽  
Positioning Algorithm ◽  
The Relationship ◽  
Improved Algorithm

In order to discuss a series of issues surrounding financial development and economic growth, it makes it more meaningful to discuss regional financial development and economic growth. This article aims to study the empirical study of the relationship between financial development and economic growth based on intelligent algorithms based on wireless network communication. In this article, a two-side positioning model is established, and the optimal solution is found for the feasible region through the particle swarm optimization algorithm. After two anchor nodes are realized, the position of the unknown node can be calculated. In addition, this article uses the Shadowing model to calculate the distance between wireless network nodes. This model is composed of path loss and occlusion factors, which can fully consider the impact of environmental factors on the signal. Experimental research data show that the improved algorithm has a positioning coverage close to 100% when the anchor node density is 30%. The improved algorithm in this article speeds up the convergence speed than positioning algorithm 1 and reduces the time by about 35%. Although the running time of the algorithm in this article is longer than that of positioning algorithm 2, the overall positioning accuracy is higher than that of the positioning algorithm, which is about 20% higher.

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