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相关概念视频

Decision Making: P-value Method01:09

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
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Decision Making: Traditional Method01:14

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
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Heuristics01:21

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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在复杂的Q-Rung Orthopair Fuzzy Hypersoft环境中使用爱因斯坦聚合运算符进行多属性决策.

Changyan Ying1,2,3, Wushour Slamu1,2,3, Changtian Ying4

  • 1School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.

Entropy (Basel, Switzerland)
|July 8, 2023
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概括

我们介绍了复杂的 q-rung orthopair fuzzy hypersoft set (Cq-ROFHSS),用于模拟不准确的人类解释. 这种先进的模糊集合理论提供了一个灵活的工具,用于处理复杂数据的决策,其性能优于现有的方法.

关键词:
爱因斯坦的聚合运算符.复杂的 q-rung 整形pair 模糊的超软套件 (Cq-ROFHSS)多个属性决策的决策.

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科学领域:

  • 模糊的集合理论 模糊的集合理论
  • 决策 决策 决策 决策
  • 信息的细节性 细节性的信息

背景情况:

  • 人类的解释往往涉及不准确和模两可.
  • 现有的模糊集合理论,如复杂的直觉和毕达哥拉模糊集合,在捕获复杂的多维数据方面存在局限性.
  • 需要更普遍的数学工具来有效处理此类数据.

研究的目的:

  • 介绍和正式化复杂的 q-rung 整形pair 模糊超软集 (Cq-ROFHSS) 的概念.
  • 扩展模糊集合理论的功能,用于建模复杂,不精确和矛盾的二维数据.
  • 根据拟议的Cq-ROFHSS框架开发多属性决策算法.

主要方法:

  • 通过结合复杂的q-rung orthopair fuzzy集和超软集的参数结构,开发了Cq-ROFHSS.
  • 为Cq-ROFHSS建立基本的集合理论运算和属性.
  • 介绍Cq-ROFHSS值的爱因斯坦运算和聚合运算符.
  • 开发使用分数和准确性函数的两个多属性决策算法.

主要成果:

  • 与现有理论相比,拟议的Cq-ROFHSS框架有效地捕捉了较高程度的不准确性和模糊性.
  • 开发的决策算法成功地在定期不一致的数据集中优先考虑理想方案.
  • 一个分布式控制系统的案例研究表明了Cq-ROFHSS方法的可行性和合理性.
  • 对比分析证实了拟议模型在主流技术上的灵活性,有效性和优越性.

结论:

  • Cq-ROFHSS是模糊集合理论的一个强大而灵活的扩展,用于处理复杂,多参数和不精确的数据.
  • 开发的决策算法提供了一种有效的方法来分析和优先考虑复杂的决策问题.
  • 这项研究为需要模拟细微和矛盾信息的应用提供了有价值的数学工具.