scholarly journals Issues of Estimating the Quality of the Facilities Management of the Emercom of Russia at the Elimination of the Emergency Situations Consequences

2018 ◽  
Vol 7 (4.38) ◽  
pp. 704
Author(s):  
Valeriy Borisovich Vilkov ◽  
Andrey Kliment’evich Chernykh ◽  
Alexander Alekseevich Tarantsev ◽  
Yuri Evgenievich Aktersky ◽  
Ilya Danilovich Cheshko
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Fuzzy Inference ◽  
Facilities Management ◽  
Inference Algorithm ◽  
Emergency Situations ◽  
The Russian Federation ◽  
Civil Defence ◽  
Sets Theory

The article deals with the problem of multiobjective optimization with regard to the decision making on the use of the forces and facilities of the EMERCOM of Russia (Ministry of the Russian Federation for Affairs for Civil Defence, Emergencies and Elimination of Consequences of Natural Disasters). The purpose of the article is to create a method for prompt and reasonable calculations when making a decision on the use of the EMERCOM forces and facilities to eliminate the consequences of emergency situations. The proposed method uses fuzzy sets, fuzzy logic, and the Mamdani fuzzy inference algorithm. The work gives a substantial example illustrating the application of the mentioned theory to solve the problem of choosing the optimal version of the task performed by the facilities of the EMERCOM of Russia. Regarding the novelty, it should be noted that the quality characteristics of the solutions are fuzzy and not unambiguously defined, and therefore allow applying the effective mathematical apparatus of fuzzy sets theory, fuzzy logic and the Mamdani fuzzy inference algorithm in solving this problem. 

scholarly journals The elimination of the consequences of transport emergencies

2021 ◽  
Vol 34 (06) ◽  
pp. 1677-1688
Author(s):  
Valeriy Borisovich Vilkov ◽  
Andrey Klimentevich Chernykh ◽  
Igor Gennadevich Malygin ◽  
Yuriy Dmitrievich Motorygin ◽  
Alexandr Vladimirovich Skripka
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Multicriteria Optimization ◽  
Optimization Problem ◽  
Fuzzy Inference ◽  
Inference Algorithm ◽  
Emergency Situations ◽  
Input Variables ◽  
Civil Defence ◽  
Multicriteria Optimization Problem

The problem of multicriteria optimization in relation to the decisions made about organizing the material and technical support for equipment and personnel of the Ministry of the Russian Federation for Civil Defence, Emergencies and Elimination of Consequences of Natural Disasters (EMERCOM of Russia) in the context of emergency response on transport has been explored in this article. The existing approaches have been indicated, and another approach to building a single generalized criterion by the given partial criteria for the multicriteria optimization problem has been proposed. The verbal statement of the considered problem of multicriteria optimization has been provided. The goal of the study is to develop a method for solving this multicriteria optimization problem using fuzzy sets, fuzzy logic, and the Mamdani's fuzzy inference algorithm. A substantial example has been provided, illustrating the application of the stated theoretical provisions for solving the problem of choosing the best option for the equipment and personnel of the EMERCOM of Russia to liquidate the consequences of emergency situations on transport. In terms of novelty, it must be noted that the indicator (output variable) and parameters (input variables) of the problem have been defined ambiguously, fuzzily, which allows to use the efficient mathematical tools of the theory of fuzzy sets, fuzzy logic, and the Mamdani's fuzzy inference algorithm to solve this problem.

scholarly journals Aplikasi Pengambilan Keputusan dengan Metode Tsukamoto pada Penentuan Tingkat Kepuasan pelanggan

CAUCHY ◽  
2015 ◽  
Vol 4 (1) ◽  
pp. 10 ◽  
Author(s):  
Venny Riana Riana Agustin ◽  
Wahyu Henky Irawan
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Weighted Average ◽  
Fuzzy Rules ◽  
Quality Of Services ◽  
Inference System ◽  
A Value ◽  
Fuzzy Logic Analysis

Tsukamoto method is one method of fuzzy inference system on fuzzy logic for decision making. Steps of the decision making in this method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules, fuzzy logic analysis, defuzzyfication (affirmation), as well as the conclusion and interpretation of the results. The results from this research are steps of the decision making in Tsukamoto method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules by the general form IF a is A THEN B is B, fuzzy logic analysis to get alpha in every rule, defuzzyfication (affirmation) by weighted average method, as well as the conclusion and interpretation of the results. On customers at the case, in value of 16 the quality of services, the value of 17 the quality of goods, and value of 16 a price, a value of the results is 45,29063 and the level is low satisfaction

A proposed method to evaluate the quality of services using Fuzzy sets theory

2012 ◽  
Vol 48 (2) ◽  
pp. 871-885 ◽  
Author(s):  
Cleriston Fritsch Damasio da Silva ◽  
Deise de Araújo Batista ◽  
Denise Dumke de Medeiros
Keyword(s):  
Fuzzy Sets ◽  
Quality Of Services ◽  
Fuzzy Sets Theory ◽  
Sets Theory

scholarly journals Perancangan Alat Pendeteksi Kualitas Minyak Goreng Dengan Parameter Viskositas Dan Densitas Mengggunakan Metode Fuzzy Logic

2021 ◽  
Vol 11 (1) ◽  
pp. 22-28
Author(s):  
Alex Surapati ◽  
Azam Zyaputra ◽  
Reza Satria Rinaldi
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Oil Quality ◽  
Test Results ◽  
Inference System ◽  
The Public ◽  
Cooking Oil ◽  
Market Needs

AbstrakThe quality of cooking oil sold in the market needs to be checked to ensure its health. cooking oil quality detector is designed to make it easier for the public to know the quality of the cooking oil. The research method is to make tools and conduct testing. The test is carried out by measuring the viscosity and density using the tool made. When the viscosity of 985 fuzzification was "good", and the density was 542.93 Kg/mL of "good" fuzzification, the fuzzification was processed by a fuzzy inference system, then defuzzification occurred in the form of oil quality results. fried "good". When the viscosity of 932 fuzzification is "sufficient", and the density is 618.69 Kg/mL of "moderate" fuzzification, a fuzzy inference system occurs, a defuzzification process is "moderate", when the viscosity of 926 fuzzification is "bad", and a density of 631.31 Kg/mL fuzzification "bad", fuzzy inference system occurs, defuzzification process occurs with "bad" results. To ensure that the results are accurate, the sample is taken to the BPOM which measures free fatty acids. From the BPOM test results converted to viscosity and density. In order to obtain an accurate conversion value between viscosity and density, it is recommended that a large number of samples be tested..Keywords: viscosity, density, fuzzy logic

Analysis of Pedagogy of Teacher's Capability to Transform Knowledge Into Practice Using Fuzzy Logic

2019 ◽  
Vol 8 (2) ◽  
pp. 16-33
Author(s):  
Jagmohan Mago ◽  
Dinesh Kumar
Keyword(s):  
Fuzzy Logic ◽  
Current Literature ◽  
Fuzzy Inference ◽  
Inference Mechanism ◽  
Common Practices ◽  
Consistent Method ◽  
Fuzzy Inference Mechanism ◽  
Personal Behavior

Current literature and common practices suggest that there is no consistent method available to analyze the performance of teachers. Due to its inherent vagueness and uncertainty, this article analyzes the effectiveness of a teacher depending upon various factors using fuzzy logic. It explains various parameters influencing professional, interpersonal and personal behavior of teachers. Secondly, a fuzzy inference mechanism is developed to decide the possible quality of teachers. The article concludes by observing that the proposed fuzzy logic based system is consistent with that judged by the experts and can be used to predict the possible quality of teachers.

Some Recent Defuzzification Methods

2017 ◽  
pp. 31-48 ◽  
Author(s):  
Harendra Kumar
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Fuzzy Number ◽  
Fuzzy Inference ◽  
Fuzzy Logic System ◽  
Single Number ◽  
Logic System

Defuzzification is a process that converts a fuzzy set or fuzzy number into a crisp value or number. Defuzzification is used in fuzzy modeling and in fuzzy control system to convert the fuzzy outputs from the systems to crisp values. This process is necessary because all fuzzy sets inferred by fuzzy inference in the fuzzy rules must be aggregated to produce one single number as the output of the fuzzy model.There are numerous techniques for defuzzifying a fuzzy set; some of the more popular techniques are included in fuzzy logic system. In the present chapter some recent defuzzification methods used in the literature are discussed with examples.

Some Recent Defuzzification Methods

Fuzzy Systems ◽  
2017 ◽  
pp. 1003-1019
Author(s):  
Harendra Kumar
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Fuzzy Number ◽  
Fuzzy Inference ◽  
Fuzzy Logic System ◽  
Single Number ◽  
Logic System

Defuzzification is a process that converts a fuzzy set or fuzzy number into a crisp value or number. Defuzzification is used in fuzzy modeling and in fuzzy control system to convert the fuzzy outputs from the systems to crisp values. This process is necessary because all fuzzy sets inferred by fuzzy inference in the fuzzy rules must be aggregated to produce one single number as the output of the fuzzy model.There are numerous techniques for defuzzifying a fuzzy set; some of the more popular techniques are included in fuzzy logic system. In the present chapter some recent defuzzification methods used in the literature are discussed with examples.

METHODOLOGY OF INCREASING THE QUALITY OF THE PROCESS OF ANALYSIS OF THE MATURE LEVEL OF DEPLOYED PROCESSES BASED ON FUZZY LOGIC MODELS

2021 ◽  
Vol 1 (27) ◽  
pp. 3-9
Author(s):  
S. A. Nazarewicz ◽  
◽  
A. V. Vinnichenko ◽  
Keyword(s):  
Fuzzy Logic ◽  
Life Cycle ◽  
Fuzzy Sets ◽  
Membership Function ◽  
Business Process ◽  
Business Processes ◽  
The State ◽  
Logic Models

The paper presents the characteristics of the maturity levels of processes in accordance with the regulations specified in GOST R ISO / IEC 15504-2-2009. The problems of identifying the states of processes during their life cycle are described, based on the rating and indirect signs characterizing the transition from one classification group to another. To complement the methodology for describing business processes and identifying the level of maturity of newly deployed processes, the apparatus of fuzzy sets is used, with the use and justification of the trapezoidal membership function. The methodology will allow to characterize the state of business processes and create a reasoned judgment about their belonging to a certain level of maturity, which will be relevant in order to make a decision on the modernization or restructuring of the business process.

Sign in / Sign up

Export Citation Format

Share Document