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基于循环神经网络的区域经济转型与升级路径

Path of Regional Economic Transformation and Upgrading Based on Recurrent Neural Network.

机构信息

School of Economics and Management, Shijiazhuang University, Zhufeng Street, Yuhua District, Shijiazhuang 050035, Hebei, China.

School of Literature and Media, Shijiazhuang University, Zhufeng Street, Yuhua District, Shijiazhuang 050035, Hebei, China.

出版信息

Comput Intell Neurosci. 2022 May 31;2022:1547837. doi: 10.1155/2022/1547837. eCollection 2022.

DOI:10.1155/2022/1547837
PMID:35685129
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9173934/
Abstract

At present, the development of the regional economy is very rapid and widespread. However, due to increasingly prominent problems such as the low level of technological innovation and the unreasonable industrial structure, the economic growth rate has declined. Therefore, it is particularly important to use the circular economy network to study the transformation and upgrading of the regional economy. It clarifies the stakeholders in the process of transformation and upgrading of manufacturing enterprises. Its benefits in the network are given, and symptoms and mobilization methods and the obstacles and solutions to the development of mobilization among various subjects are drawn. In addition, it also emphasizes the equivalence between intelligent products and human subjects in this network. Because of the intelligence carried by products under the current background, diversified connotations and functions are becoming more and more abundant. The empirical results show that the pulling coefficients of residents' consumption level, the development of modern service industry, and urbanization rate to economic growth are 0.1812, 0.7165, and 0.1635, respectively, while the pulling coefficient of Gini coefficient to economic growth is -0.1785.

摘要

目前,区域经济发展非常迅速且广泛。然而,由于技术创新水平低和产业结构不合理等问题日益突出,经济增长速度有所下降。因此,利用循环经济网络来研究区域经济的转型和升级尤为重要。它阐明了制造企业转型和升级过程中的利益相关者,给出了其在网络中的利益,并描绘了各种主体在发展动员中出现的症状、动员方法以及障碍和解决方案。此外,它还强调了网络中智能产品与人的主体之间的等价性。由于当前背景下产品所具有的智能,其多样化的内涵和功能变得越来越丰富。实证结果表明,居民消费水平、现代服务业发展和城镇化率对经济增长的拉动系数分别为 0.1812、0.7165 和 0.1635,而基尼系数对经济增长的拉动系数为-0.1785。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c110/9173934/7efbf1641f61/CIN2022-1547837.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c110/9173934/7ec522e71409/CIN2022-1547837.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c110/9173934/f74c25f95f7e/CIN2022-1547837.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c110/9173934/93f195223e69/CIN2022-1547837.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c110/9173934/54fb34494802/CIN2022-1547837.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c110/9173934/26c73c64d0cf/CIN2022-1547837.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c110/9173934/c40dba2e979b/CIN2022-1547837.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c110/9173934/7ca23fe02300/CIN2022-1547837.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c110/9173934/acad072c1784/CIN2022-1547837.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c110/9173934/7efbf1641f61/CIN2022-1547837.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c110/9173934/7ec522e71409/CIN2022-1547837.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c110/9173934/f74c25f95f7e/CIN2022-1547837.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c110/9173934/93f195223e69/CIN2022-1547837.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c110/9173934/54fb34494802/CIN2022-1547837.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c110/9173934/26c73c64d0cf/CIN2022-1547837.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c110/9173934/c40dba2e979b/CIN2022-1547837.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c110/9173934/7ca23fe02300/CIN2022-1547837.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c110/9173934/acad072c1784/CIN2022-1547837.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c110/9173934/7efbf1641f61/CIN2022-1547837.009.jpg

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