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Updated: Jan 8, 2026

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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
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一个新的算法,用于模型不确定性降低形模糊断层树风险评估的模型不确定性
Yuanyuan Zhang1, Long Zhao1, Tao Zhang2
1School of Environmental and Safety Engineering, Liaoning Petrochemical University, Fushun, Liaoning, China.
PloS one
|December 15, 2025
概括
这项研究引入了一种用于形模糊断层树分析的新算法,显著减少了风险评估中的模型不确定性. 该方法提高了核能和化学加工等行业中复杂系统的可靠性.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 风险管理 风险管理
背景情况:
- 复杂系统的风险评估依赖于模糊的断层树.
- 传统方法在概率计算和模型不确定性方面面临挑战,影响可靠性.
研究的目的:
- 提出一种新的算法,用于减少形模糊断层树分析中的模型不确定性.
- 提高复杂系统中风险评估的准确性和可靠性.
主要方法:
- 开发了一个新的算法,利用切割设定定理来进行梯形模糊断层树分析.
- 直接运行在形模糊数字上,没有模糊化,保留模糊信息.
- 适应只有OR,只有AND和混合的OR/AND门逻辑.
主要成果:
- 实现了45.25%的模型不确定性降低,超过现有方法 (36.65%).
- 证明99.40%与基准文献一致,证实准确性.
- 在±15%的输入扰动下,表现出异常稳定性,输出变化<1%.
结论:
- 这种新的算法为高风险行业的风险评估提供了可靠和可扩展的解决方案.
- 该方法有效地减少了模型不确定性,并提高了模糊故障树分析的准确性.
- 经过验证的稳定性和稳定性使其适用于数据不确定性的真实应用.
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