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Discernibility matrix method is an important method to design algorithm for computing the core based on information entropy. In this method, the core is found by discovering all discernibility elements of discernibility matrix. So this method is very time consuming. To improve the efficient of computing the core based on information entropy, the core of the simplified decision which is the same as...
This paper aimed to establish a method of risk assessment based on grey decision making. Based on the characteristics of IT projects' risk, the paper defined five main factors that influence the success rate of IT projects and mixed the total risk to a matrix. By applying the grey relational analysis to measure the matrix's grey relational grade, in terms of which we can establish the evaluation guideline...
This article mainly introduces the method of TOPSIS and describes in much detail the application of TOPSIS in seeking the optimum method of blended coal by confirming the weight of index with entropy. A blended coal combustion in power plant issue as an example, optimum method is selected under the condition that four aims and four kinds of blended coals. The method can combine qualitative analysis...
In the paper, we present a general method for obtaining the OWA operator weights via extreme point approach. The extreme points are determined by the intersection of an attitudinal character constraint and a fundamental ordered weight simplex. The extreme points are completely identified by a proposed algorithm and the OWA operator weights can be expressed by a convex combination of the identified...
The key of attribute reduction based on rough set is find the core attributes. Most existing works are mainly based on Hu's discernibility matrix. Till now, there are three kinds of core attributes: Hu's core based on discernibility matrix (denoted by Core1(C)), core based on positive region (denoted by Core2(C)), and core based on information entropy (denoted by Core3(C)). Some researchers have been...
Affinity propagation is an impressive clustering algorithm which was published in Science, 2007. However, the original algorithm couldn't cope with part known data directly. Focusing on this issue, a semi-supervised scheme called incremental affinity propagation clustering is proposed in the paper. In the scheme, the pre-known information is represented by adjusting similarity matrix. Moreover, an...
In this paper, the weights of all indexes reflecting regional using FDI effect are put up by the entropy method objectively. Then the distance between the real effect of using FDI and ideal solution is calculated through TOPSIS, which is a technique for order preference by similarity to ideal solution.
This paper proposes an approach to customer value assessment based on Max-Entropy and TOPSIS, while customerspsila information are interval numbers. The decision matrix in the form of interval is normalized and transformed into a definite one based on maximizing the entropy of the decision matrix; The weights of attributes are then determined, which are used in assessing the alternatives based on...
It has recently been shown that there is a connection between Cayley's hypdeterminant and the principal minors of a symmetric matrix. With an eye towards characterizing the entropy region of jointly Gaussian random variables, we obtain three new results on the relationship between Gaussian random variables and the hyperdeterminant. The first is a new (determinant) formula for the 2times2times2 hyperdeterminant...
Entropy calculated on EEG has been shown to be a useful indicator of effects from insufficient oxygen supply. In this paper, the estimation of entropy is based on transition matrices instead of probability density functions. It is shown that the separation of sleep stages thereby can be improved. This suggests that by including time information given by the transition matrix in entropy estimates of...
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