Object Monitoring Under Z-Information Based on Rating Points

Poleshchuk, Olga

doi:10.1007/978-3-030-51156-2_139

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…In [28], the distance between two Z-numbers Z 1 = B1 , R1 , Z 2 = B2 , R2 with aggregating indicators (b 1 , r 1 ), (b 2 , r 2 ) of fuzzy numbers B1 , R and B2 , R2 is determined:…”

Section: Basic Concepts and Definitionsmentioning

confidence: 99%

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Clustering Z-Information Based on Semantic Spaces

Poleshchuk

2021

Lecture Notes in Networks and Systems

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In the paper a model for clustering Z-information is developed. Zinformation is represented by the sets of Z-numbers. Each of the sets is an expert criterion model for assessing a certain characteristic. The first and the second components of Z-numbers are the values of full orthogonal semantic spaces. The first components of Z-numbers are the formalizations of the levels of the linguistic scale used by experts to assess the characteristic. The second components of Znumbers are the formalizations of the levels of the linguistic scale used by experts to assess the reliability of their results. The distances between expert criteria (sets of Z-numbers) are found using aggregating indicators for Z-numbers. Based on pairwise distances between sets of Z-numbers (expert criteria), fuzzy binary relation of similarity is determined on a set of criteria. A fuzzy conformity relation is constructed using the transitive closure of the determined fuzzy similarity relation. The constructed fuzzy conformity relation is decomposed into equivalence relations. Depending on α-levels of fuzzy conformity relations, the set of expert criteria, represented by sets of Z-numbers, can be divided into clusters of criteria similar among themselves with α-levels of confidence.

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“…In [28], the distance between two Z-numbers Z 1 = B1 , R1 , Z 2 = B2 , R2 with aggregating indicators (b 1 , r 1 ), (b 2 , r 2 ) of fuzzy numbers B1 , R and B2 , R2 is determined:…”

Section: Basic Concepts and Definitionsmentioning

confidence: 99%

“…[20][21][22][23]. The developed methods and models have been successfully applied to solve decision-making problems [24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Clustering Z-Information Based on Semantic Spaces

Poleshchuk

2021

Lecture Notes in Networks and Systems

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“…An expert provides an assessment of each planting with one of the reliability levels U, NVL, L, VL, EL, which correspond to fuzzy numbers Rk , k = 1, 5. If the expert estimated M plants of the i-th species by the level A l , l = 1, 7 with reliability Riml = (r iml1 , r iml2 , r iml3 ), i = 1, n, m = 1, M , l = 1, 7, where fuzzy number Riml , i = 1, n, m = 1, M , l = 1, 7 equals to one of the fuzzy number Rk , k = 1, 5, then r il1 = max m (r mil1 ), r il2 = max m (r mil2 ), r il3 = max m (r mil3 ) [15].…”

Section: Monitoring Stability Of Plant Species Under Z-informationmentioning

confidence: 99%

“…In [15], the definition of weighted point c(based on the definition of weighted segment [23]) of fuzzy number Ã = (a 1 , a 2 , a L , a R ) is given:…”

Section: Monitoring Stability Of Plant Species Under Z-informationmentioning

confidence: 99%

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Monitoring Stability of Plant Species to Harmful Urban Environment Under Z-information

Poleshchuk

2021

Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation

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The paper developed a model for monitoring of plant species in urban environment under Z-information. The main task of green species monitoring in the city is to determine the types of plants that are the most adapted to the ecological conditions of the urban environment in order to plan various activities carried out at landscaping objects. When assessing the state of plantings, even a highly qualified expert cannot always be absolutely sure of assigning plants to one of the status categories. The information coming from experts is given with a certain degree of reliability, which must be taken into account. Previously, to determine the state of plant species, the reliability of the incoming information was never taken into account due to the lack of a mathematical apparatus. To formalize expert assessments of plants, the author uses Z-numbers, the components of which are the values of semantic spaces. To determine these values, a method developed by the author earlier is used. Assessments of the state of plant species are found by comparative analysis with an ideal reference state. For this, a distance between the state of the plant species, represented as a set of Z-numbers, and the ideal state is determined. A numerical example of monitoring the state of plant species on the Boulevard Ring avenue of Moscow is given.

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The Analysis of Student Performance During Face-to-Face and Distance Learning Under Z-Information

Tumor

Poleshchuk

2022

Lecture Notes in Electrical Engineering

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Object Monitoring Under Z-Information Based on Rating Points

Cited by 7 publications

References 13 publications

Clustering Z-Information Based on Semantic Spaces

Clustering Z-Information Based on Semantic Spaces

Monitoring Stability of Plant Species to Harmful Urban Environment Under Z-information

The Analysis of Student Performance During Face-to-Face and Distance Learning Under Z-Information

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