企业财务管理的优化模型利用遗传算法和模糊逻辑.
Sujuan Wang1, Musadaq Mansoor2
1School of Accounting, Zhengzhou University of Economics and Business, Zhengzhou, Henan, China.
PeerJ. Computer science
|June 26, 2025
概括
本研究介绍了一种新的等级强化学习与模糊推理 (HRL-FR) 模型,用于优化企业财务管理和增强风险预测. HRL-FR模型显著提高了财务决策和准确性.
科学领域:
- 财务管理 财务管理
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 企业财务管理在优化风险预测模型方面面临着复杂的问题.
- 现有的模型经常在动态的金融环境中扎不确定性.
研究的目的:
- 开发和验证一个优化企业财务管理的先进模型.
- 提高风险预测性能和决策能力.
主要方法:
- 开发了一种用于降低成本和利用资本的多目标数学模型.
- 集成的遗传算法与反向传播 (BP) 神经网络进行参数优化.
- 提出了一个基于模糊推理 (HRL-FR) 的等级强化学习模型.
主要成果:
- 该HRL-FR模型在预测企业财务管理信息方面表现出卓越的准确性.
- 工作资本资产比率和债务对股权比率等关键金融变量被确定为显著的.
- 使用Compustat和CRSP数据集验证的有效性,显示了提高利能力和效率.
结论:
- HRL-FR模型为优化财务管理和降低风险提供了一个强大的工具.
- 该研究强调了人工智能驱动的方法在复杂的金融环境中的潜力.
- 调查结果为企业的战略决策提供了宝贵的见解.
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