A novel model termed a bipolar complex fuzzy N-soft set(BCFN-SS)is initiated for tackling information that involves positive and negative aspects,the second dimension,and parameterised grading simultaneously.The theor...A novel model termed a bipolar complex fuzzy N-soft set(BCFN-SS)is initiated for tackling information that involves positive and negative aspects,the second dimension,and parameterised grading simultaneously.The theory of BCFN-SS is the generalisation of two various theories,that is,bipolar complex fuzzy(BCF)and N-SS.The invented model of BCFN-SS helps decision-makers to cope with the genuine-life dilemmas containing BCF information along with parameterised grading at the same time.Further,various algebraic operations,including the usual type of union,intersection,complements,and a few others types,are invented.Certain primary operational laws for BCFNSS are also invented.Moreover,a technique for order preference by similarity to the ideal solution(TOPSIS)approach is devised in the setting of BCFN-SS for managing strategic decision-making(DM)dilemmas containing BCFN-SS information.Keeping in mind the usefulness and benefits of the TOPSIS approach,two various types of TOPSIS approaches in the environment of BCFN-SS are devised and then a numerical example for exposing the usefulness of the devised TOPSIS approach is interpreted.To disclose the prominence and benefits of the devised work,the devised approaches with numerous prevailing work are compared.展开更多
Ensemble learning for anomaly detection of data structured into a complex network has been barely studied due to the inconsistent performance of complex network characteristics and the lack of inherent objective funct...Ensemble learning for anomaly detection of data structured into a complex network has been barely studied due to the inconsistent performance of complex network characteristics and the lack of inherent objective function. We propose the intuitionistic fuzzy set（IFS）-based anomaly detection, a new two-phase ensemble method for anomaly detection based on IFS, and apply it to the abnormal behavior detection problem in temporal complex networks.Firstly, it constructs the IFS of a single network characteristic, which quantifies the degree of membership,non-membership and hesitation of each network characteristic to the defined linguistic variables so that makes the unuseful or noise characteristics become part of the detection. To build an objective intuitionistic fuzzy relationship, we propose a Gaussian distribution-based membership function which gives a variable hesitation degree. Then, for the fuzzification of multiple network characteristics, the intuitionistic fuzzy weighted geometric operator is adopted to fuse multiple IFSs and to avoid the inconsistence of multiple characteristics. Finally, the score function and precision function are used to sort the fused IFS. Finally, we carry out extensive experiments on several complex network datasets for anomaly detection, and the results demonstrate the superiority of our method to state-of-the-art approaches, validating the effectiveness of our method.展开更多
The evaluation method and its accuracy for evaluating complex systems are considered. In order to evaluate accurately complex systems, the existed evaluating methods are simply analyzed, and a new comprehensive evalua...The evaluation method and its accuracy for evaluating complex systems are considered. In order to evaluate accurately complex systems, the existed evaluating methods are simply analyzed, and a new comprehensive evaluating method is developed. The new method is integration of Delphi approach, analytic hierarchy process, gray interconnect degree and fuzzy evaluation (DHGF). Its theory foundation is the meta-synthesis methodology from qualitative analysis to quantitative analysis. According to fuzzy set approach, using the methods of concordance of evaluation, redundant verify, double models redundant, and limitations of the method etc, the accuracy of evaluating method of DHGF is estimated, and a practical example is given. The result shows that using the method to evaluate complex system projects is feasible and credible.展开更多
模糊目标信息系统(fuzzy objective information systems,简称FOISs)在许多实际应用中存在,这种系统上的知识简化不能采用Pawlak信息系统上的约简方法.因此,提出了模糊目标信息系统上的a分布约简、a最大分布约简、a分配约简、粗糙分布约...模糊目标信息系统(fuzzy objective information systems,简称FOISs)在许多实际应用中存在,这种系统上的知识简化不能采用Pawlak信息系统上的约简方法.因此,提出了模糊目标信息系统上的a分布约简、a最大分布约简、a分配约简、粗糙分布约简,并给出了它们的性质以及与Pawlak信息系统上约简的关系,同时也给出了这些约简的判定定理、对应的可辨识矩阵、约简公式.这些约简推广了Pawlak信息系统上的知识约简方法,为模糊目标信息系统上的知识发现和基于粗糙模糊规则的模糊概念分类器提供了新的低复杂性手段.展开更多
文摘A novel model termed a bipolar complex fuzzy N-soft set(BCFN-SS)is initiated for tackling information that involves positive and negative aspects,the second dimension,and parameterised grading simultaneously.The theory of BCFN-SS is the generalisation of two various theories,that is,bipolar complex fuzzy(BCF)and N-SS.The invented model of BCFN-SS helps decision-makers to cope with the genuine-life dilemmas containing BCF information along with parameterised grading at the same time.Further,various algebraic operations,including the usual type of union,intersection,complements,and a few others types,are invented.Certain primary operational laws for BCFNSS are also invented.Moreover,a technique for order preference by similarity to the ideal solution(TOPSIS)approach is devised in the setting of BCFN-SS for managing strategic decision-making(DM)dilemmas containing BCFN-SS information.Keeping in mind the usefulness and benefits of the TOPSIS approach,two various types of TOPSIS approaches in the environment of BCFN-SS are devised and then a numerical example for exposing the usefulness of the devised TOPSIS approach is interpreted.To disclose the prominence and benefits of the devised work,the devised approaches with numerous prevailing work are compared.
基金Supported by the National Natural Science Foundation of China under Grant No 61671142the Fundamental Research Funds for the Central Universities under Grant No 02190022117021
文摘Ensemble learning for anomaly detection of data structured into a complex network has been barely studied due to the inconsistent performance of complex network characteristics and the lack of inherent objective function. We propose the intuitionistic fuzzy set（IFS）-based anomaly detection, a new two-phase ensemble method for anomaly detection based on IFS, and apply it to the abnormal behavior detection problem in temporal complex networks.Firstly, it constructs the IFS of a single network characteristic, which quantifies the degree of membership,non-membership and hesitation of each network characteristic to the defined linguistic variables so that makes the unuseful or noise characteristics become part of the detection. To build an objective intuitionistic fuzzy relationship, we propose a Gaussian distribution-based membership function which gives a variable hesitation degree. Then, for the fuzzification of multiple network characteristics, the intuitionistic fuzzy weighted geometric operator is adopted to fuse multiple IFSs and to avoid the inconsistence of multiple characteristics. Finally, the score function and precision function are used to sort the fused IFS. Finally, we carry out extensive experiments on several complex network datasets for anomaly detection, and the results demonstrate the superiority of our method to state-of-the-art approaches, validating the effectiveness of our method.
文摘The evaluation method and its accuracy for evaluating complex systems are considered. In order to evaluate accurately complex systems, the existed evaluating methods are simply analyzed, and a new comprehensive evaluating method is developed. The new method is integration of Delphi approach, analytic hierarchy process, gray interconnect degree and fuzzy evaluation (DHGF). Its theory foundation is the meta-synthesis methodology from qualitative analysis to quantitative analysis. According to fuzzy set approach, using the methods of concordance of evaluation, redundant verify, double models redundant, and limitations of the method etc, the accuracy of evaluating method of DHGF is estimated, and a practical example is given. The result shows that using the method to evaluate complex system projects is feasible and credible.
文摘模糊目标信息系统(fuzzy objective information systems,简称FOISs)在许多实际应用中存在,这种系统上的知识简化不能采用Pawlak信息系统上的约简方法.因此,提出了模糊目标信息系统上的a分布约简、a最大分布约简、a分配约简、粗糙分布约简,并给出了它们的性质以及与Pawlak信息系统上约简的关系,同时也给出了这些约简的判定定理、对应的可辨识矩阵、约简公式.这些约简推广了Pawlak信息系统上的知识约简方法,为模糊目标信息系统上的知识发现和基于粗糙模糊规则的模糊概念分类器提供了新的低复杂性手段.
