对于直觉模糊集的非线性严格距离和相似性测量,用于模式分类和医学诊断
Xinxing Wu1, Huan Tang2, Zhiyi Zhu2
1School of Mathematics and Statistics, Guizhou University of Finance and Economics, Guiyang, 550025, Guizhou, China. wuxinxing5201314@163.com.
Scientific reports
|August 25, 2023
概括
本研究引入了对于直觉模糊集 (IFS) 的新型非线性距离和相似度. 这些新措施解决了现有方法的局限性,并提高了分类和决策任务的性能.
科学领域:
- 模糊的数学 模糊的数学
- 信息理论 信息理论
- 决策科学 决策科学 决策科学
背景情况:
- 对于直觉模糊集 (IFS) 的现有距离和相似度有局限性,特别是在处理高度犹和区分不同的IFS时.
- 马哈塔和熊猫 (2021) 方法表现出缺点,例如任何直觉模糊数的距离值始终是最大的.
研究的目的:
- 为直觉模糊集合 (IFS) 提出新的非线性严格距离和相似度.
- 解决和克服先前方法中发现的缺点,特别是Mahanta和Panda (2021) 的方法.
- 确保拟议的测量符合公理定义,并保持距离和相似度测量的可取性质.
主要方法:
- 为IFS开发非线性参数距离测量方法.
- 坚持严格的直觉模糊距离的公理定义的证明.
- 导出一个双相似度测量和诱导的满足公理定义.
主要成果:
- 拟议的非线性距离测量有效地区分IFS,即使是那些犹不决的人.
- 新的测量满足了严格的直觉模糊距离,相似度和直觉模糊的公理定义.
- 与现有方法相比,这些方法通过解决已识别的缺点,显示出更高的性能.
结论:
- 拟议的非线性距离和相似度措施为处理直观模糊信息提供了强大而有效的方法.
- 这些措施适用于各种现实问题,包括模式分类,决策和医学诊断.
- 该研究在模糊集合理论及其应用领域取得了重大进展.
相关概念视频
Interval Level of Measurement
15.3K
For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...
15.3K
Introduction to Nonparametric Statistics
752
Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
One of...
752
Classification of Illness
7.6K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
7.6K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
155
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
155
Functional Classification of Joints
4.2K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
4.2K
Linear Approximation in Time Domain
101
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
101


