Добірка наукової літератури з теми "Euclidean distance degree"

In a Group Decision Making problem, several people try to reach a single common decision by selecting one of the possible alternatives according to their respective preferences. So, a consensus process is performed in order to increase the level of accord amongst people, called experts, before obtaining the final solution. Improving the consensus degree as much as possible is a very interesting task in the process. In the evaluation of the consensus degree, the measurement of the distance representing disagreement among the experts’ preferences should be considered. Different distance functions have been proposed to implement in consensus models. The Euclidean distance function is one of the most commonly used. This paper analyzes how to improve the consensus degrees, obtained through the Euclidean distance function, when the preferences of the experts are slightly modified by using one of the properties of the Uniform distribution. We fulfil an experimental study that shows the betterment in the consensus degrees when the Uniform extension is applied, taking into account different number of experts and alternatives.

The aim of this paper is to propose a satisfactory degree method by using nonlinear programming for solving multi-attribute decision making (MADM) problems in which ratings of alternatives on attributes is expressed via interval-valued intuitionistic fuzzy (IVIF) sets and preference information on attributes is incomplete. Concretely, a nonlinear programming model is firstly explored to determine the satisfactory degree which is the ratio of the square of the weight Euclidean distance between an alternative and the IVIF negative ideal solution (IVIFNIS) to the sum of the square of the weight Euclidean distance between the IVIF negative ideal solution (IVIFNIS) and the IVIF positive ideal solution (IVIFPIS). Another nonlinear programming model is also developed to obtain satisfactory intuitionistic fuzzy sets, and then the general satisfactory degrees of the satisfactory intuitionistic fuzzy sets are used to generate the ranking order of the alternatives. Finally, a real example is employed to verify the applicability of the proposed approach and illustrate its practicality and effectiveness.

One of most prominent features that social networks or e-commerce sites now provide is recommendation of items. However, the recommendation task is challenging as high degree of accuracy is required. This paper analyzes the improvement in recommendation of movies using Fuzzy Inference System (FIS) and Adaptive Neuro Fuzzy Inference System (ANFIS). Two similarity measures have been used: one by taking account similar users' choice and the other by matching genres of similar movies rated by the user. For similarity calculation, four different techniques, namely Euclidean Distance, Manhattan Distance, Pearson Coefficient and Cosine Similarity are used. FIS and ANFIS system are used in decision making. The experiments have been carried out on Movie Lens dataset and a comparative performance analysis has been reported. Experimental results demonstrate that ANFIS outperforms FIS in most of the cases when Pearson Correlation metric is used for similarity calculation.

The proposed section of the collective monograph is devoted to the industrial development analysis of the Right-Bank Ukraine three provinces’ (Kyiv, Podillia, Volyn) with a total area of 154643 sq. km and a population of 4683860 in the mid-1840s. That was the time when the first clear signs of commodity industrial production appeared in a large number of local landowners’ estates, took place the spread of manufacturing, focused mainly on local raw materials and the local market. It was in the mid-1840s that not only descriptive but also statistical sources of historical and economic orientation became widespread, which is greatly expanding the researcher’s ability to create reconstructive models of ancient times economic processes. The purpose of our study is to identify the nature and degree of industrial specialization of the Right-Bank Ukraine’ 36 districts in various industries, marketability of production through its volume, fixed in monetary terms. The research methodology is determined by the features of the information base, which combines descriptive and statistical sources. Accordingly, first we give a general description of the local industry, its raw material base, organization and technology, the approximate range of consumers. In the second stage, based on the statistical data presented in the relevant tables, we use multidimensional statistical cluster analysis to make a meaningful classification of 36 districts by the nature and direction of their industrial specialization. As a result, we obtain a model which elements are grouped by common qualitative characteristics, the distance (degree of similarity or difference) between objects and groups can be measured by multidimensional scaling (in our case – the distance in Euclidean space). Macrogroup A from 7 districts of the northern part of the region with a population nearby 799600 was received 85,8% of industrial revenues from the processing of livestock products. Macrogroup E united 14 districts, mainly in the southern zone of the Dnieper Right-Bank, with a population of 1616370. It was characterized by in-depth specialization in the plant origin products processing, from which 96,7% of industrial profits were received. Macrogroup C represented by one district of Kyiv with the central regional city and a total population of 176280. Only here 76,5% of industrial profits came from the processing raw materials of mineral origin. Macrogroup D includes 8 districts in the south of the Right Bank with a population of 1090600 people and natural conditions equally suitable for crop and livestock production. Hence the balance of the processing industry and revenues from it – 48,5% of processing of crop products and 44,5% of processing of livestock products. Macrogroup B included 6 districts with a population of 816350, whose farms did not have a narrow production specialization: 26,1% of industrial profits came from processing of plant products, 33,6% from processing of livestock products, 40,3% of industrial profits from processing of minerals. The practical significance of our study is that the results obtained can be used in the construction of broader paleoeconomic reconstructions, in the educational process, in writing scientific articles and monographs. The originality and scientific novelty of the work lies in the formulation of the problem, the methodology used, the results obtained. Such a study for the Right-Bank Ukraine region of the mid-1840s is conducted for the first time.

Sustainable supplier selection is one of the most important decisions for companies in sustainable supply chain management. Therefore, sustainable supplier performance evaluation and selection are of great importance in terms of economic, environmental, and social aspects of sustainable development. Sustainable supplier selection problem can be considered as a multiple-criteria decision-making (MCDM) problem. In the MCDM problems, decision-makers evaluate conflicting criteria and alternatives according to their own preferences/judgments. One of the most well-known MCDM methods is the technique for order preference by similarity to an ideal solution (TOPSIS). Neutrosophic set (NS) is an extension of fuzzy set where each element of the universe has the degrees of truth, indeterminacy, and falsity. A single-valued neutrosophic set (SVNS) is a special case of the neutrosophic set. This chapter aims to evaluate sustainable supplier selection in a Turkish discount market chain using the single-valued neutrosophic TOPSIS method based on normalized Euclidean and Hamming distances.

This paper extends the technique for order preference by similarity to ideal solution (TOPSIS) for solving multi-attribute group decision making (MAGDM) problems under Atanassov intuitionistic fuzzy set (IFS) environments. In this methodology, weights of attributes and ratings of alternatives on attributes are extracted from fuzziness inherent in decision data and making process and described using Atanassov IFSs. An Euclidean distance measure is developed to calculate the differences between alternatives for each decision maker and an Atanassov IFS positive ideal solution (IFSPIS) as well as an Atanassov IFS negative ideal-solution (IFSNIS). Degrees of relative closeness to the Atanassov IFSPIS for all alternatives with respect to each decision maker in the group are calculated. Then all decision makers in the group may be regarded as “attributes” and a corresponding classical MADM problem is generated and hereby solved by the TOPSIS. The proposed methodology is validated and compared with other similar methods. A numerical example is examined to demonstrate the implementation process of the methodology proposed in this paper.

In this paper, a pattern recognition method is put forward to identify damping coefficients of rod pumping system of directional well by using characteristics space mapping. The 24-direction chain code is presented to encode the curve of dynamometer card. The parametric equation of the dynamometer card curve is transformed into Fourier series whose coefficients can be computed according to the curve’s chain codes. By means of those Fourier coefficients, shape characteristics of the curve are extracted. Euclidean distance is introduced as the measurement of similar degree between the shape char

One of the important social roles the elderly perform is that of grandparents. Our study aims to identify groups of Russian regions with similar models of grandparental activity. The research focuses only on grandmothers. To determine these models, we applied the hierarchical cluster analysis. We used indicators that characterize potential (based on the age criterion) and active (based on intensive involvement in caring for grandchildren) grandparenting in Russian regions. In the process of clustering, we use the growth rates of active grandmothers in the total number of potential grandmothers