适应性信念规则基础建模复杂的工业系统基于西格函数的模型.
Haolan Huang1, Shucheng Feng1, Jingying Li1
1The School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China.
Entropy (Basel, Switzerland)
|November 26, 2025
概括
本研究介绍了一种可靠的非线性信念规则基础 (R-NBRB) 模型,以改善复杂系统中的非线性拟合和不确定性表示. 在R-NBRB模型显著减少错误的工业应用程序,如石油泄漏检测.
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
- 人工智能的人工智能
背景情况:
- 现有的信念规则基础 (BRB) 模型在复杂的工业系统中与非线性动态,不确定性表示和参数优化作斗争.
- 多因素非线性关系和固有的不确定性对传统建模方法构成重大挑战.
研究的目的:
- 开发一种改进和可靠的非线性信念规则基础 (R-NBRB) 建模方法.
- 为了增强非线性拟合,不确定性表示和参数优化能力.
- 解决现有的BRB模型在处理复杂的工业系统动态方面的局限性.
主要方法:
- 用一个平滑的非线性S函数取代了线性推理机制,以便更好地适应非线性动态.
- 使用可靠性评估方法和通过证据推理 (ER) 算法集成的数据,可靠性和专家知识来量化属性可靠性.
- 应用了共变矩阵适应进化策略 (CMA-ES) 算法来优化推理参数以减少决策偏差.
主要成果:
- R-NBRB模型在复杂的工业场景中表现出有效性,石油管道泄漏检测验证了这一点.
- 与标准BRB模型相比,实现了0.2569的平均平方误差 (MSE),比标准BRB模型减少了28.24%.
- 成功地整合了数据,可靠性和专家知识,通过信念度表达了不确定性.
结论:
- 拟议的R-NBRB方法为建模具有非线性关系和不确定性的复杂工业系统提供了卓越的性能和适应性.
- 非线性函数的集成,可靠性评估,ER算法和CMA-ES优化为可靠的建模提供了一个强大的框架.
- 该方法有效地减少了决策偏见并提高了准确性,展示了其在关键工业监控任务中的实际应用性.
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