相关实验视频
Updated: Jun 16, 2026

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A Two-interval Forced-choice Task for Multisensory Comparisons
Published on: November 9, 2018
一个动态的混合权重框架,用于多标准决策的教学有效性评估:集成间隔估值的直觉模糊的AHP和触发
1School of Electrical and Photoelectric Engineering, West Anhui University, Lu'an 237012, China.
Entropy (Basel, Switzerland)
|February 27, 2026
概括
本研究引入了一个动态的混合权重框架,用于复杂的决策,增强专家的不确定性表示和调整指标的重要性,使用区间值的直观模糊分析层次过程 (IVIF-AHP) 和基于的校正.
科学领域:
- 决策科学 决策科学
- 人工智能的人工智能
- 模糊系统 (Fuzzy Systems) 是一个模糊系统.
背景情况:
- 多标准决策 (MCDM) 面临着专家不确定性和动态指标重要性的挑战.
- 像AHP和权衡这样的传统方法在量化专家犹和静态权重分配方面扎.
研究的目的:
- 为不确定性下的MCDM问题提出动态混合权重框架.
- 改善专家判断的表现,并适应不断变化的数据分布.
主要方法:
- 集成间隔值直观模糊分析层次过程 (IVIF-AHP) 来建模专家不确定性 (会员,非会员,犹).
- 开发一个由触发的动态聚变策略,将信息和适应性重量调整的变量系数结合起来.
- 在分层模糊的决策结构和模糊的综合评估过程中实施.
主要成果:
- 拟议的框架有效地模拟专家的不确定性,并动态调整主观和客观来源之间的权重.
- 通过教学有效性评估的案例研究来验证,证明了减轻重量刚性和评估通货膨胀.
- 与传统方法相比,改善了诊断分辨率和决策稳定性.
结论:
- 触发的IVIF-AHP框架为不确定性下的动态MCDM提供了强大的和数学上合理的解决方案.
- 该方法在实践中可用,并显示出在复杂的评估和决策支持系统中更广泛使用的潜力.
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