# [1]李燕燕,杨昊天,曾玙璠.基于随机森林MOPSO的城市最优资本结构分析[J].郑州大学学报(工学版),2019,40(04):14.[doi:10.13705/j.issn.1671-6833.2019.04.028] 　Li Yanyan,Yang Haotian,Zeng Yufan.Urban Optimal Capital Structure Analysis based on Random Forest and Multi-objective Particle Swarm Optimization[J].Journal of Zhengzhou University (Engineering Science),2019,40(04):14.[doi:10.13705/j.issn.1671-6833.2019.04.028] 点击复制 基于随机森林MOPSO的城市最优资本结构分析() 分享到： var jiathis_config = { data_track_clickback: true };

40卷

2019年04期

14

2019-07-10

## 文章信息/Info

Title:
Urban Optimal Capital Structure Analysis based on Random Forest and Multi-objective Particle Swarm Optimization

1. 郑州大学商学院;2. 郑州大学产业技术研究院电气工程学院;3. 英国利物浦大学数学科学系
Author(s):
1. School of Business, Zhengzhou University; 2. School of Electrical Engineering, Institute of Industrial Technology, Zhengzhou University; 3. Department of Mathematical Sciences, University of Liverpool

Keywords:
DOI:
10.13705/j.issn.1671-6833.2019.04.028

A

Abstract:
Urban capital structure was a complex?problem affected by multi-factors and multi-objective particle.This paper attempt ed to explore a scientific and appropriate d algorithm to construct the optimal capital structure model under the influence of multi-objective and multi-factors to analyze the situation of urban capital structure.First, the data in history could find the relationship among features of the data in history by using the regression characteristics of random forest. Then, the multi-objective particle swarm optimization algorithm was used to find values of the features that achieve the best results according to the existing relationship features. Then finding the most correlate data from the historical data based on the best eigenvalues of these effects. Therefore, the cities and the years with relatively better capital structure allocations are analyzed. We could play a good role in the reference and development of each city by continuously learning these superior structural configurations

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