The age of the Longzhaogou and Jixi Groups of coal measures in eastern Heilongjiang were previouslyconsidered to be Jurassic or mainly Jurassic. But there occur Middle Barremian-Early Albian Aucellina(bivalvia) fossil...The age of the Longzhaogou and Jixi Groups of coal measures in eastern Heilongjiang were previouslyconsidered to be Jurassic or mainly Jurassic. But there occur Middle Barremian-Early Albian Aucellina(bivalvia) fossils in the Upper Yunshan Formation of the Longzhaogou Group and the Lower Chengzihe For-mation of the Jixi Group, and the Qihulin Formation of the Longzhaogou Group yields Early Cretaceousbivalve and ammonite fossils. Consequently, the geological ages of the two groups are mainly, or even all, Ear-ly Cretaceous.展开更多
The aggregation of fuzzy opinions is an important component of group decision analysis with fuzzy information. This paper proposes two new approaches for the assessment of the weights to be associated with fuzzy opini...The aggregation of fuzzy opinions is an important component of group decision analysis with fuzzy information. This paper proposes two new approaches for the assessment of the weights to be associated with fuzzy opinions. These approaches involve,respectively,the minimization of the sum of squared differences between the individual weighted fuzzy opinion and the weighted mean value of all fuzzy opinions,which is called the weighted minimum variance method (WMVM),and the minimax difference of any two adjacent individual weighted fuzzy opinions,which is called the mean value minimax differences method (MVMDM). The two approaches are developed and numerical examples are presented to illustrate their simplicity and effectiveness in aggregating fuzzy opinions.展开更多
In this article, a new method for aggregating fuzzy individual opinions into a group consensus opinion is proposed. To obtain the aggregation weights of each individual opinion, a consistency index of each expert with...In this article, a new method for aggregating fuzzy individual opinions into a group consensus opinion is proposed. To obtain the aggregation weights of each individual opinion, a consistency index of each expert with the other experts is introduced based on similarity and distance. The importance of each expert is also taken into consideration in the process of aggregation. Finally, a numerical example is presented to illustrate the efficiency of the procedure.展开更多
基金Director Foundation of Nanjing Institute of Geology and Palaeontology,Academin Sinica.
文摘The age of the Longzhaogou and Jixi Groups of coal measures in eastern Heilongjiang were previouslyconsidered to be Jurassic or mainly Jurassic. But there occur Middle Barremian-Early Albian Aucellina(bivalvia) fossils in the Upper Yunshan Formation of the Longzhaogou Group and the Lower Chengzihe For-mation of the Jixi Group, and the Qihulin Formation of the Longzhaogou Group yields Early Cretaceousbivalve and ammonite fossils. Consequently, the geological ages of the two groups are mainly, or even all, Ear-ly Cretaceous.
文摘The aggregation of fuzzy opinions is an important component of group decision analysis with fuzzy information. This paper proposes two new approaches for the assessment of the weights to be associated with fuzzy opinions. These approaches involve,respectively,the minimization of the sum of squared differences between the individual weighted fuzzy opinion and the weighted mean value of all fuzzy opinions,which is called the weighted minimum variance method (WMVM),and the minimax difference of any two adjacent individual weighted fuzzy opinions,which is called the mean value minimax differences method (MVMDM). The two approaches are developed and numerical examples are presented to illustrate their simplicity and effectiveness in aggregating fuzzy opinions.
文摘In this article, a new method for aggregating fuzzy individual opinions into a group consensus opinion is proposed. To obtain the aggregation weights of each individual opinion, a consistency index of each expert with the other experts is introduced based on similarity and distance. The importance of each expert is also taken into consideration in the process of aggregation. Finally, a numerical example is presented to illustrate the efficiency of the procedure.
