Study on Self-adaptive Step Tangent Optimization Algorithm and Application for Optimizing Listed Companies' Corporate Performance
Authors: 
Tian Wang, Jianbang LinAuthors Info & Claims
Pages 479 - 482
Abstract
In the process of national economic and social development, the new energy industry has played an important role in optimizing and upgrading the China's economic system. It is particularly important to correctly predict the corporate performance of new energy enterprises. In this study, the new energy enterprises in the listed companies are the research objects. With the rapid development of artificial intelligence technology recently, scholars have proposed different intelligent approaches to improve the model accuracy. In this study, self-adaptive step tangent optimization (SASTO) algorithm is proposed and applied to optimize new energy enterprises’ corporate performance. The result shows SASTO can effectively improve the prediction effectiveness and efficiency of the model.
References
[1]
People's Republic of China. Proposals for formulating the 14th Five-Year Plan for National Economic and Social Development and the Long-Range Objectives Through the Year 2035. http://www.gov.cn/xinwen/2021-03/13/content_5592681.htm, 2021.
[2]
China Energy Network. During the 14th Five-Year Plan period, new energy sources will increase in both quantity and quality. http://newenergy.giec.cas.cn/cyxx/202012/t20201219_602696.html, 2020.
[3]
J. Kennedy and R. C. Eberhart, “Particle Swarm Optimization,” IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, 1995.
[4]
Q. K. Pan, H. Y. Sang, J. H. Duan, and L. Gao, “An improved fruit fly optimization algorithm for continuous function optimization problems,” Knowledge-Based Systems, vol. 062, pp. 69-83, 2014.
[5]
J. H. Friedman, “Multivariate Adaptive Regression Splines,” The Annals of Statistics, vol. 019, no. 1, pp. 1-67, 1991.
[6]
K. E. Sveiby, “Measuring intangibles and intellectual capital- an emerging first standard,” Knowledge Management: Classic and Contemporary Works, pp. 344-436, 2001.
[7]
B. Lev and P. Zarowin, “The boundaries of financial reporting and how to extend them,” Journal of Accounting Research, vol. 037, no. 2, pp.353-385, 2009.
Recommendations
Management of Listed Companies Holding Corporate Performance Associated with Empirical Research
ICIII '11: Proceedings of the 2011 International Conference on Information Management, Innovation Management and Industrial Engineering - Volume 01Management is the management holding the owner of a listed company, holding shares of listed companies, in order to address the separation of ownership and management under the conditions of management incentives. In this paper, annual reports of listed ...
Study on Innovation Performance of Big Data and Artificial Intelligence Listed Companies
ISBDAI '18: Proceedings of the International Symposium on Big Data and Artificial IntelligenceIn the rapidly changing world economy, big data and artificial intelligence listed companies may be the future industrial giants. The main body of such enterprises is the emerging technology industry. Innovation represents the theme of today's world ...
Self- assessment Performance Measurement in Construction Companies: An Application of the EFQM Excellence Model on Processes and Customer Stages
AbstractThe EFQM Excellence Model was introduced in 1990s as a framework for assessing applications for The UK Excellence Award and it is the most widely-used Business Excellence Framework in Europe, with over 30,000 businesses using the Excellence Model ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
October 2021
3136 pages
ISBN:9781450385046
DOI:10.1145/3495018
Copyright © 2021 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 14 March 2022
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
AIAM2021
AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture
October 23 - 25, 2021
Manchester, United Kingdom
Acceptance Rates
Overall Acceptance Rate 100 of 285 submissions, 35%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 15Total Downloads
- Downloads (Last 12 months)2
- Downloads (Last 6 weeks)0
Reflects downloads up to 05 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format