Let c: VxV [right arrow] [0, i] be the attribute value contradiction (dissimilarity) degree function (that we introduce now for the first time) between any two attribute values [v.sub.1] and [v.sub.2], denoted by
c([v.sub.1], [v.sub.2]) = 0, the contradiction degree between the same attribute values is zero;
She put 'Ounces', 'Tons', and 'Pounds' cards under the measurable attribute Weight (Figure 4).
Teacher: So I'll give you the next one [Teacher places the card with a measurable attribute 'Volume' on the table].
GOAT: ATTRIBUTE: Unity BORN: 2015, 2003, 1991, 1979, 1967 THIS YEAR: Take a long look at your own well-being this year .
MONKEY: ATTRIBUTE: Changeability BORN: 2016, 2004, 1992, 1980, 1968 THIS YEAR: this is the year you learn to not let your past hold you back.
Let [mathematical expression not reproducible] denote the interval decision matrix, where, [mathematical expression not reproducible] is the interval consequence for alternative [[??].sub.ij] with respect to attribute [c.sub.j], B = [[[b.sub.ij]].sub.mxn], j = 1, ..., n.
Different experts express their subjective preference information on attributes in the following formats, i.e., preference orderings, linguistic terms, interval numbers, and inequality constraints among the attribute weights, as stated in Table 1.
In this paper, the numerical attributes are divided into three categories : (1) nominal attribute, it refers to only a set of numerical symbols, such as ID card number, which does not have a meaningful order, and is not quantitative; (2) ordinal attribute, its possible values have the meaningful orders or ranking, but the difference between successive is unknown; (3) general numerical attribute, its possible values are measurable, such as age and height.
The calculation method of e_ij can be referred to (3), where n is the number of data tuple and count(A = a_i) is the count of value a J which appeared in the A attribute.
Use of multi attribute transforms to predict log properties from seismic data.
Keywords: Artificial Neural Network; Petrophysical analysis; Porosity prediction; seismic attributes, Prospective zones.
Indices and criteria for risk assessment of water inrush are based on the statistical information about geology in karst tunnels, and several influencing factors of water inrush are selected as the attribute evaluation indices.
So, when the attribute recognition model is applied to comprehensive risk evaluation of water inrush, the evaluation results of attribute recognition model based on linear measure function often have relatively large errors.
Using the encryption algorithm, the value of each attribute
group i is encrypted to the cipher-text C([M.sub.i]]).