一个新的混合神经网络框架,灵感来自生物系统,用于先进的财务预测
Chuchu Rao1, Tianju Xue2, Mingqi Kan3
1School of Mechanical and Electrical Engineering, Quzhou College of Technology, Quzhou, 324000, China.
Scientific reports
|October 22, 2025
概括
本研究介绍了一种量子灵感的黑猩猩优化算法与内核极端学习机器 (QChOA-KELM),用于改进金融风险预测. 这种新的方法提高了金融市场的准确性和效率.
科学领域:
- 计算金融是指计算金融.
- 机器学习 机器学习
- 优化算法 优化算法
背景情况:
- 在现代市场中,准确的金融风险预测至关重要.
- 现有的方法在效率,复杂性和性能方面扎.
- 这些局限性需要先进的预测建模技术.
研究的目的:
- 提出一个新的量子灵感的黑猩猩优化算法与内核极端学习机器 (QChOA-KELM).
- 提高金融风险预测准确度和模型稳定性.
- 为金融风险评估提供高效的计算框架.
主要方法:
- 量子计算原理与元启发式优化集成.
- 应用QChOA来优化内核极端学习机 (KELM) 的参数.
- 使用 Kaggle 来源的金融风险数据集进行实验验证.
主要成果:
- 与基线KELM相比,QChOA-KELM的准确性得到了10.3%的改善.
- 该模型在关键指标上表现至少比传统方法高9%.
- 观察到预测性能和模型稳定性的显著提升.
结论:
- QChOA-KELM方法为金融风险评估提供了一个卓越的计算框架.
- 提出的方法实现了高预测性能,同时保持了计算效率.
- 这项研究通过创新的算法集成,推进了金融风险预测领域.
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