摘要
为有效解决序决策信息系统的模糊信息、偏好信息和噪声信息的问题,结合单值中智集与概率粗糙集模型,构建一种基于改进得分函数的新单值中智概率粗糙集模型.首先,综合考虑单值中智数的模糊性和人为偏好性定义了一种改进的得分函数,以此确定对象间的优势关系;其次,为提高模型的容错能力,引入条件概率阈值构建了单值中智概率粗糙集模型,依据概率下、上近似约简的性质设计了一种基于辨识矩阵的近似约简方法与规则提取算法.应用实例验证了新提出方法的有效性和适用性.
To effectively address the issues of fuzzy information,preference information,and noise information in sequential decision information systems,a new single-valued intelligent probability rough set model is constructed by integrating singleton fuzzy sets and probability rough set models,based on an improved scoring function.Firstly,considering the fuzziness of singleton fuzzy numbers and subjective preferences,an improved scoring function is defined to establish dominance relationships among objects.Secondly,to enhance the fault tolerance of the model,a single-valued intelligent probability rough set model is introduced by incorporating a conditional probability threshold.A reduction method and rule extraction method based on discernibility matrix are designed according to the properties of probability lower and upper approximation.The effectiveness and applicability of the proposed method are validated through application examples.
作者
王聪
骆公志
WANG Cong;LUO Gongzhi(School of Management,Nanjing University of Posts and Telecommunications,Nanjing 210003)
出处
《系统科学与数学》
北大核心
2025年第8期2484-2499,共16页
Journal of Systems Science and Mathematical Sciences
基金
国家自然科学基金项目(72171124)
江苏高校哲学社会科学研究重大项目(2021SJZDA129)
江苏省研究生科研创新计划项目(KYCX23_0936)资助课题。
关键词
概率粗糙集
得分函数
单值中智集
近似约简
辨识矩阵
Probability rough set
scoring function
single-valued intuitionistic set
approximate reduction
discernibility matrix
