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优化反向传播神经网络用于企业财务管理中的风险预测
1School of Economics and Management, Wuhan Railway Vocational College of Technology, Wuhan, 430205, China. lingzi_gu@outlook.com.
Scientific reports
|November 7, 2023
概括
本研究介绍了一种使用逆向传播辅助神经网络 (ORP-BNN) 的最佳风险预测模型,以改善企业财务管理. 该模型增强了风险预测和资金平衡,以改善财务决策.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 财务管理 财务管理
背景情况:
- 企业金融需要持续优化和风险管理.
- 现有的模型难以准确预测金融失衡.
- 人工智能和机器学习为金融风险评估提供了先进的能力.
研究的目的:
- 为最佳风险预测 (ORP-BNN) 引入一个新的反向传播辅助神经网络 (BNN).
- 设计一个模型来预先验证财务不平衡并优化资金分配.
- 通过改善风险缓解来加强企业财务管理.
主要方法:
- 使用反向传播辅助神经网络 (ORP-BNN) 开发最佳风险预测模型.
- 实施线性降雪模型用于资金分配和限制决策.
- 利用梯度损失函数和历史财务数据用于模型训练和优化.
主要成果:
- 该ORP-BNN模型有效地预测和预验证金融不平衡.
- 线性降雪模型准确地确定了资金分配与限制的意义.
- 该模型证明了企业财务规划中的成功风险减轻.
结论:
- 拟议的ORP-BNN模型显著提高了企业的金融风险管理.
- 该模型为优化资金配置和最大限度地降低财务风险提供了一个强大的框架.
- 这种方法支持更知情,更快速的财务决策.
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