SLPDBO-BP:数据资产价值的高效估值模型
Cuiping Zhou1, Shaobo Li1,2, Cankun Xie1
1Guizhou University, State Key Laboratory of Public Big Data, Guiyang, Guizhou, China.
PeerJ. Computer science
|June 26, 2025
概括
本研究介绍了SLPDBO-BP模型用于数据资产价值评估,比传统方法提高了准确性和效率. 这种新的方法通过优化评估过程来增强数据资产估值.
科学领域:
- 数据科学数据科学数据科学
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 传统的数据资产价值评估方法存在主观性和低效.
- 准确的数据资产估值对于数据因子化和战略开发至关重要.
研究的目的:
- 引入一种新的SLPDBO-BP模型,用于客观和高效的数据资产价值评估.
- 增强全球优化能力,以提高评估准确度.
主要方法:
- 开发了SLPDBO算法,通过整合正弦混沌映射,Levie飞行和适应性体重变化.
- 使用20个测试函数对现有优化算法进行SLPDBO性能评估.
- 将SLPDBO与反向传播 (BP) 结合起来,创建用于数据资产估值的SLPDBO-BP模型.
主要成果:
- SLPDBO-BP模型显著提高了数据资产评估的准确性.
- 与DBO-BP.相比,实现了平均绝对误差 (MAE) 的减少35.1%,根平均平方误差 (RMSE) 的减少37.6%,平均绝对百分比误差 (MAPE) 的减少38.7%.
- 证明了提高评估效率和优越的模拟效果.
结论:
- SLPDBO-BP模型为数据资产价值评估提供了更准确,更有效的解决方案.
- 提出的优化策略增强了模型克服传统方法局限性的能力.
- SLPDBO-BP为可靠的数据资产估值提供了一个强大的框架.
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