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加强自动驾驶汽车的旅行咨询区,使用以区间值为基础的毕达哥拉斯模糊Yager决策
Samera Nawaz1, Abdul Razaq2, Ghaliah Alhamzi3
1Department of Mathematics, Government College University, Faisalabad, 38000, Pakistan.
Scientific reports
|February 9, 2026
概括
新型间隔值的毕达哥拉斯模糊 (IVPF) 雅格聚合运营商 (AO) 增强自动驾驶汽车 (AV) 的决策. 这些新运营商和评分功能改善了AV的多属性决策 (MADM),特别是在选择旅行区时.
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:
- 自动驾驶汽车 (AV) 为减少交通事故提供了巨大的潜力.
- 在自动驾驶系统中,有效的决策 (DM) 对安全和效率至关重要.
- 现有的多属性决策 (MADM) 方法面临着不确定性的挑战.
研究的目的:
- 为AV决策引入新的间隔估值毕达哥拉斯模糊 (IVPF) 雅格聚合运算符 (AOs).
- 为排名IVPF号码 (IVPFNs) 开发一个新的评分功能.
- 通过使用先进的模糊逻辑技术,解决AV系统中的MADM挑战.
主要方法:
- 开发两个新的雅格公司:IVPFYWA和IVPFYWG运营商.
- 制定了一个新的评分函数来排名IVPFNs.
- 探讨被提议的操作者的基本特性 (无力性,单调性,局限性).
- 在IVPF框架内设计一个用于解决MADM问题的算法.
主要成果:
- 拟议的IVPF Yager AOs有效地解决了模两可的决策问题.
- 新的评分功能使IVPFNs的强大排名成为可能.
- 开发的算法成功地解决了AV环境中的MADM问题.
- 适用于选择最佳AV旅行咨询区的应用证明了该方法的有效性.
结论:
- 新的IVPF Yager AOs为AVs提供了对MADM的优越方法.
- 提出的方法提高了自主系统的决策稳定性和准确性.
- 对比分析验证了开发的策略在现有技术上的优势.
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