为同时应对不确定信息表示与风险信息融合对群决策带来的挑战,构建一种三角模糊不完备三支群决策方法,并将其应用于糖尿病诊断决策。首先,针对信息不确定性蕴含的模糊性和不完备性,分别引入三角模糊集和不完备信息系统的概念。通过与多...为同时应对不确定信息表示与风险信息融合对群决策带来的挑战,构建一种三角模糊不完备三支群决策方法,并将其应用于糖尿病诊断决策。首先,针对信息不确定性蕴含的模糊性和不完备性,分别引入三角模糊集和不完备信息系统的概念。通过与多粒度三支决策结合,构建了可调多粒度三角模糊概率粗糙集模型。然后,根据离差最大化法计算属性权重与专家权重,结合ELECTRE(elimination et choice translating reality)方法建立了三角模糊多属性群决策方法。最后,通过对糖尿病患者数据的案例分析和评估,验证了所提方法的可行性和有效性。该方法不仅从不确定信息表示、风险信息融合和最优粒度选择的视角丰富了多粒度三支群决策理论,而且推动了糖尿病智能诊断方面的应用。展开更多
本文针对偏好信息由中智集(NS)表示的多属性群决策问题(MAGDM)进行研究,将静态决策环境下的中智集扩展为动态决策环境下的非线性中智集,并开发了相应的投影模型和集结算法。首先,本文给出了非线性中智集的定义及运算法则。然后,将非线...本文针对偏好信息由中智集(NS)表示的多属性群决策问题(MAGDM)进行研究,将静态决策环境下的中智集扩展为动态决策环境下的非线性中智集,并开发了相应的投影模型和集结算法。首先,本文给出了非线性中智集的定义及运算法则。然后,将非线性中智数投影为三维空间中的曲线,用曲线之间所围成曲面的面积大小来描述决策者偏好之间的差异,从而完成非线性中智集空间投影模型的建立。最后,开发基于模拟植物生长算法(PGSA)的空间曲线集结算法,通过寻找与所有偏好曲线围成曲面面积之和最小的最优集结曲线来完成非线性中智集的集结,并结合TOPSIS算法完成多属性群决策问题中的方案排序工作。文章的实验部分通过一个具体案例来说明本文所提出方法的有效性。This paper investigates the problem of multi-attribute group decision making (MAGDM) where preference information is represented by a neutrosophic set (NS). It extends the concept of neutral set from static decision environments to nonlinear neutrosophic set in dynamic decision environments, and develops a corresponding projection model and aggregation algorithm. Firstly, we provide the definition and algorithm for nonlinear neutrosophic sets. Then, we project the nonlinear neutral set onto a curve in three-dimensional space, describing differences in decision makers’ preferences through the surface area between curves. This allows us to establish a projection model for the space of nonlinear neutral sets. Finally, we develop a space curve aggregation algorithm based on the plant growth simulation algorithm (PGSA). By identifying an optimal aggregation curve with minimal sum of surface areas between all preference curves, we assemble the nonlinear neutral set and combine it with TOPSIS algorithm to sort schemes in multi-attribute group decision making problems. The experimental section demonstrates the effectiveness of our proposed method through a specific case.展开更多
文摘为同时应对不确定信息表示与风险信息融合对群决策带来的挑战,构建一种三角模糊不完备三支群决策方法,并将其应用于糖尿病诊断决策。首先,针对信息不确定性蕴含的模糊性和不完备性,分别引入三角模糊集和不完备信息系统的概念。通过与多粒度三支决策结合,构建了可调多粒度三角模糊概率粗糙集模型。然后,根据离差最大化法计算属性权重与专家权重,结合ELECTRE(elimination et choice translating reality)方法建立了三角模糊多属性群决策方法。最后,通过对糖尿病患者数据的案例分析和评估,验证了所提方法的可行性和有效性。该方法不仅从不确定信息表示、风险信息融合和最优粒度选择的视角丰富了多粒度三支群决策理论,而且推动了糖尿病智能诊断方面的应用。
文摘本文针对偏好信息由中智集(NS)表示的多属性群决策问题(MAGDM)进行研究,将静态决策环境下的中智集扩展为动态决策环境下的非线性中智集,并开发了相应的投影模型和集结算法。首先,本文给出了非线性中智集的定义及运算法则。然后,将非线性中智数投影为三维空间中的曲线,用曲线之间所围成曲面的面积大小来描述决策者偏好之间的差异,从而完成非线性中智集空间投影模型的建立。最后,开发基于模拟植物生长算法(PGSA)的空间曲线集结算法,通过寻找与所有偏好曲线围成曲面面积之和最小的最优集结曲线来完成非线性中智集的集结,并结合TOPSIS算法完成多属性群决策问题中的方案排序工作。文章的实验部分通过一个具体案例来说明本文所提出方法的有效性。This paper investigates the problem of multi-attribute group decision making (MAGDM) where preference information is represented by a neutrosophic set (NS). It extends the concept of neutral set from static decision environments to nonlinear neutrosophic set in dynamic decision environments, and develops a corresponding projection model and aggregation algorithm. Firstly, we provide the definition and algorithm for nonlinear neutrosophic sets. Then, we project the nonlinear neutral set onto a curve in three-dimensional space, describing differences in decision makers’ preferences through the surface area between curves. This allows us to establish a projection model for the space of nonlinear neutral sets. Finally, we develop a space curve aggregation algorithm based on the plant growth simulation algorithm (PGSA). By identifying an optimal aggregation curve with minimal sum of surface areas between all preference curves, we assemble the nonlinear neutral set and combine it with TOPSIS algorithm to sort schemes in multi-attribute group decision making problems. The experimental section demonstrates the effectiveness of our proposed method through a specific case.
