Because of the complexity of decision-making environment, the uncertainty of fuzziness and the uncertainty of grey maybe coexist in the problems of multi-attribute group decision making. In this paper, we study the problems of multi-attribute group decision making with hybrid grey attribute data (the precise values, interval numbers and linguistic fuzzy variables coexist, and each attribute val...

Because of the complexity of decision-making environment, the uncertainty of fuzziness and the uncertainty of grey maybe coexist in the problems of multi-attribute group decision making. In this paper, we study the problems of multi-attribute group decision making with hybrid grey attribute data (the precise values, interval numbers and linguistic fuzzy variables coexist, and each attribute val...

Grey numbers facilitate the representation of uncertainty not only for elements of a set, but also the set itself as a whole. This paper utilizes the notion of possibility degree from grey system theory coupled with the idea of dominance relation and partial order set (poset) from rough theory to represent uncertain information in a manner that maintains the degree of uncertainty of information...

This paper describes a generalisation of the binary N tuple technique originally described by Bledsoe and Browning (1). The binary N tuple technique has commonly been used for the classification (2) and pre-processing (3) of binary images. The extension to the method described here allows grey level images of objects to be classified using the N tuple method without first having to convert the ...

Current grey clustering analysis methods have some defects. So, this paper proposes a prediction model based on improved grey clustering analysis. Firstly, it constructs the grey classical domain and the grey sector domain based on prediction subjects and data and according to relevant theory about grey clustering analysis. Secondly, it categorizes samples according to features of prediction su...

This paper describes a generalisation of the binary N tuple technique originally described by Bledsoe and Browning (1). The binary N tuple technique has commonly been used for the classification (2) and pre-processing (3) of binary images. The extension to the method described here allows grey level images of objects to be classified using the N tuple method without first having to convert the ...

For any integer $kgeq 1$ and any graph $G=(V,E)$ with minimum degree at least $k-1$‎, ‎we define a‎ ‎function $f:Vrightarrow {0,1,2}$ as a Roman $k$-tuple dominating‎ ‎function on $G$ if for any vertex $v$ with $f(v)=0$ there exist at least‎ ‎$k$ and for any vertex $v$ with $f(v)neq 0$ at least $k-1$ vertices in its neighborhood with $f(w)=2$‎. ‎The minimum weight of a Roman $k$-tuple dominatin...

Aiming at solving multi-criteria decision problems in power quality, a new decision-making method based on grey correlation degree and TOPSIS is proposed. On the basis of introducing the concept of grey correlation degree into traditional TOPSIS, a new relative similarity degree is established to decide power quality by combining Euclidean distance with grey correlation degree. Error originated...

Based on grey language multi-attribute group decision making, a kernel and grey scale scoring function is put forward according to the definition of grey language and the meaning of the kernel and grey scale. The function introduces grey scale into the decision-making method to avoid information distortion. This method is applied to the grey language hesitant fuzzy group decision making, and th...