scholarly journals A Single-Valued Neutrosophic Extension of the EDAS Method

Axioms ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 245
Author(s):  
Dragiša Stanujkić ◽  
Darjan Karabašević ◽  
Gabrijela Popović ◽  
Dragan Pamučar ◽  
Željko Stević ◽  
...  
Keyword(s):  
Membership Function ◽  
Evaluation Criteria ◽  
Membership Functions ◽  
Complex Problems ◽  
Level Of Satisfaction ◽  
Complex Evaluation

This manuscript aims to propose a new extension of the EDAS method, adapted for usage with single-valued neutrosophic numbers. By using single-valued neutrosophic numbers, the EDAS method can be more efficient for solving complex problems whose solution requires assessment and prediction, because truth- and falsity-membership functions can be used for expressing the level of satisfaction and dissatisfaction about an attitude. In addition, the indeterminacy-membership function can be used to point out the reliability of the information given with truth- and falsity-membership functions. Thus, the proposed extension of the EDAS method allows the use of a smaller number of complex evaluation criteria. The suitability and applicability of the proposed approach are presented through three illustrative examples.

scholarly journals A Novel Approach for Assessing the Reliability of Data Contained in a Single Valued Neutrosophic Number and its Application in Multiple Criteria Decision Making

2020 ◽  
pp. 22-29
Author(s):  
admin admin ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Decision Making ◽  
Evaluation Criteria ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Membership Functions ◽  
Neutrosophic Sets ◽  
Numerical Illustration ◽  
Novel Approach ◽  
Complex Evaluation ◽  
The Many

Multiple criteria decision making is one of the many areas where neutrosophic sets have been successfully applied to solve various problems so far. Compared to a fuzzy set, and similar sets, neutrosophic sets use more membership functions which makes them suitable for using complex evaluation criteria in multiple criteria decision making. On the other hand, the application of three membership functions makes evaluation somewhat more complex compared to evaluation using fuzzy sets. The reliability of the data used to solve a problem can have an impact on the selection of the appropriate solution/alternative. Therefore, this paper discusses an approach that can be used to assess the reliability of information collected by surveying respondents. The usability of the proposed approach is demonstrated in the numerical illustration of the supplier selection.

AUTOMATIC DEVELOPMENT OF FUZZY MEMBERSHIP FUNCTIONS ON HEPATITIS PATIENTS DATA USING PARTICLE SWARM OPTIMIZATION (PSO)

2015 ◽  
Vol 77 (22) ◽  
Author(s):  
Candra Dewi ◽  
Ratna Putri P.S ◽  
Indriati Indriati
Keyword(s):  
Particle Swarm Optimization ◽  
Membership Function ◽  
Fuzzy System ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Membership Functions ◽  
Swarm Optimization ◽  
Global Weight ◽  
The Status ◽  
And Function

Information about the status of disease (prognosis) for patients with hepatitis is important to determine the type of action to stabilize and cure this disease. Among some system, fuzzy system is one of the methods that can be used to obtain this prognosis. In the fuzzification process, the determination of the exact range of membership function will influence the calculation of membership degree and of course will affect the final value of fuzzy system. This range and function can usually be formed using intuition or by using an algorithm. In this paper, Particle Swarm Optimization (PSO) algorithm is implemented to form the triangular membership functions in the case of patients with hepatitis. For testing process, this paper conducts four scenarios to find the best combination of PSO parameter values . Based on the testing it was found that the best parameters to form a membership function range for the hepatitis data is about 0.9, 0.1, 2, 2, 100, 500 for inertia max, inertia min, local ballast constant, global weight constant, the number of particles, and maximum iterations respectively.  

scholarly journals A Partitioning Based Algorithm to Fuzzy Tricluster

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yongli Liu ◽  
Tengfei Yang ◽  
Lili Fu
Keyword(s):  
Membership Function ◽  
Fuzzy Clustering ◽  
Experimental Studies ◽  
Three Dimensional ◽  
Three Dimensions ◽  
Membership Functions ◽  
Automatic Categorization ◽  
Dimensional Cube ◽  
Data Collections ◽  
Multiple Clusters

Fuzzy clustering allows an object to exist in multiple clusters and represents the affiliation of objects to clusters by memberships. It is extended to fuzzy coclustering by assigning both objects and features membership functions. In this paper we propose a new fuzzy triclustering (FTC) algorithm for automatic categorization of three-dimensional data collections. FTC specifies membership function for each dimension and is able to generate fuzzy clusters simultaneously on three dimensions. Thus FTC divides a three-dimensional cube into many little blocks which should be triclusters with strong coherent bonding among its members. The experimental studies onMovieLensdemonstrate the strength of FTC in terms of accuracy compared to some recent popular fuzzy clustering and coclustering approaches.

scholarly journals Membership Functions for Fuzzy Focal Elements

2016 ◽  
Vol 26 (3) ◽  
pp. 395-427 ◽  
Author(s):  
Sebastian Porębski ◽  
Ewa Straszecka
Keyword(s):  
Membership Function ◽  
Data Driven ◽  
Membership Functions ◽  
Data Set ◽  
Dempster Shafer Theory ◽  
Elimination Algorithm ◽  
Rule Sets ◽  
Shafer Theory ◽  
Iris Data

Abstract The paper presents a study on data-driven diagnostic rules, which are easy to interpret by human experts. To this end, the Dempster-Shafer theory extended for fuzzy focal elements is used. Premises of the rules (fuzzy focal elements) are provided by membership functions which shapes are changing according to input symptoms. The main aim of the present study is to evaluate common membership function shapes and to introduce a rule elimination algorithm. Proposed methods are first illustrated with the popular Iris data set. Next experiments with five medical benchmark databases are performed. Results of the experiments show that various membership function shapes provide different inference efficiency but the extracted rule sets are close to each other. Thus indications for determining rules with possible heuristic interpretation can be formulated.

MULTI-CHANNEL FUZZY QUEUING SYSTEMS AND MEMBERSHIP FUNCTIONS OF RELATED FUZZY SERVICES AND FUZZY INTER-ARRIVAL TIMES

2008 ◽  
Vol 25 (05) ◽  
pp. 697-713 ◽  
Author(s):  
ÖZLEM AYDIN ◽  
AYŞEN APAYDIN
Keyword(s):  
Membership Function ◽  
Measurement Errors ◽  
Waiting Times ◽  
Membership Functions ◽  
Queuing Systems ◽  
Queuing Model ◽  
Arrival Times ◽  
Queuing Models ◽  
Service Times ◽  
The One

Queuing models need well defined knowledge on arrivals and service times. However, in real applications, because of some measurement errors or some loss of information, it is hard to achieve deterministic knowledge. Non-deterministic knowledge interferes or complicates analysis of the queuing model. Additionally, when the customers are asked about their impressions on waiting times or service times, mostly the answers are linguistic expressions like "I waited too much", "service was fast", and that the responses are. Linguistic statements and ill defined data make the sense of imprecision in the model. In this study, arrivals and service times are defined as fuzzy numbers in order to represent this imprecision. Fuzzy multi-channel queuing systems and membership functions are introduced in defining the arrivals and service times. Besides, a new membership function based on a probability function is studied. Fuzzy queuing characteristics are calculated via different membership functions and the results are compared on simulations. Among models it is found that, Generalized Beta Distribution membership function is the one that minimized the queuing characteristics.

scholarly journals ОСОБЛИВОСТІ СУБ’ЄКТНОСТІ ДІВЧАТ ЮНАЦЬКОГО ВІКУ З РІЗНИМ СТАВЛЕННЯМ ДО ЗОВНІШНОСТІ

2019 ◽  
pp. 95-118
Author(s):  
Г. Меднікова ◽  
H. Даниленко
Keyword(s):  
Evaluation Criteria ◽  
Physical Appearance ◽  
External Pressure ◽  
Movie Industry ◽  
Teenage Girls ◽  
Level Of Satisfaction ◽  
Pressure Development ◽  
Single Factor Analysis ◽  
Definition Of ◽  
The Given

Relevance of the problem:Attitude to appearance of a modern day person is strongly presses by mass media communications, advertisement, movie industry, and so forth. Making appearance of high value and significance leads to dissatisfaction of many people with their proper physical appearance, psychological and psychic diseases against the given dissatisfaction actualizing the issue of searching the factors of resilience to the external pressure, development of abilities to create proper evaluation criteria and activity direction. Aimoftheresearch: Definition of specificity of the teenage girls’ subjectivity with different correlation of satisfaction and anxiety of proper appearance. Methods: Two blocks of methods used: block of attitude to appearance and subjectivity block (213 girls of 19-21 years old); cluster and single-factor analysis of variance, Kruskall-Wallis test. Results of the research. There are distinctions by every measure of attitude to appearance in the groups of girls different by satisfaction and anxiety of proper physical appearance. The highest measures of subjectivity concerning its components were found in the group of girls satisfied and unanxious of their physical appearance, and the lowest ones – in the group of girls with average and low level of satisfaction and anxiety of their physical appearance. The girls anxious of their appearance without regard to the level of satisfaction with their appearance differ by the most expressed external casual orientation and quasi-reflection, the least expressed introspection, behavior orientation to social desirability, external regularities and demands, tendency to fixation on the present events, defensive closedness of self-attitude.

scholarly journals Learning Membership Functions in a Function-Based Object Recognition System

1995 ◽  
Vol 3 ◽  
pp. 187-222 ◽  
Author(s):  
K. Woods ◽  
D. Cook ◽  
L. Hall ◽  
K. Bowyer ◽  
L. Stark
Keyword(s):  
Object Recognition ◽  
Membership Function ◽  
Learning Algorithm ◽  
Physical Property ◽  
Recognition System ◽  
Tree Structure ◽  
Membership Functions ◽  
Recognition Systems ◽  
Membership Value ◽  
The Membership Function

Functionality-based recognition systems recognize objects at the category level by reasoning about how well the objects support the expected function. Such systems naturally associate a ``measure of goodness'' or ``membership value'' with a recognized object. This measure of goodness is the result of combining individual measures, or membership values, from potentially many primitive evaluations of different properties of the object's shape. A membership function is used to compute the membership value when evaluating a primitive of a particular physical property of an object. In previous versions of a recognition system known as Gruff, the membership function for each of the primitive evaluations was hand-crafted by the system designer. In this paper, we provide a learning component for the Gruff system, called Omlet, that automatically learns membership functions given a set of example objects labeled with their desired category measure. The learning algorithm is generally applicable to any problem in which low-level membership values are combined through an and-or tree structure to give a final overall membership value.

scholarly journals Detection of waterborne bacteria using Adaptive Neuro-Fuzzy Inference System

2020 ◽  
Vol 158 ◽  
pp. 05002
Author(s):  
Farhan Mohammad Khan ◽  
Smriti Sridhar ◽  
Rajiv Gupta
Keyword(s):  
Water Quality ◽  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Quality Analysis ◽  
Hybrid Technique ◽  
Membership Functions ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques

The detection of waterborne bacteria is crucial to prevent health risks. Current research uses soft computing techniques based on Artificial Neural Networks (ANN) for the detection of bacterial pollution in water. The limitation of only relying on sensor-based water quality analysis for detection can be prone to human errors. Hence, there is a need to automate the process of real-time bacterial monitoring for minimizing the error, as mentioned above. To address this issue, we implement an automated process of water-borne bacterial detection using a hybrid technique called Adaptive Neuro-fuzzy Inference System (ANFIS), that integrates the advantage of learning in an ANN and a set of fuzzy if-then rules with appropriate membership functions. The experimental data as the input to the ANFIS model is obtained from the open-sourced dataset of government of India data platform, having 1992 experimental laboratory results from the years 2003-2014. We have included the following water quality parameters: Temperature, Dissolved Oxygen (DO), pH, Electrical conductivity, Biochemical oxygen demand (BOD) as the significant factors in the detection and existence of bacteria. The membership function changes automatically with every iteration during training of the system. The goal of the study is to compare the results obtained from the three membership functions of ANFIS- Triangle, Trapezoidal, and Bell-shaped with 35 = 243 fuzzy set rules. The results show that ANFIS with generalized bell-shaped membership function is best with its average error 0.00619 at epoch 100.

Meaning and Measurement

2012 ◽  
Author(s):  
Gary Goertz ◽  
James Mahoney
Keyword(s):  
Membership Function ◽  
Fuzzy Set ◽  
Quantitative Research ◽  
Fundamental Principle ◽  
The Other ◽  
Membership Functions ◽  
Research Paradigm ◽  
Fuzzy Set Analysis ◽  
Quantitative Indicators ◽  
The Relationship

This chapter examines how translation problems are manifested across the qualitative and quantitative cultures for issues related to concepts and measurement. In the quantitative research paradigm, one speaks of variables and indicators. X and Y are normally latent, unobserved variables for which one needs (quantitative) indicators. In practice, quantitative scholars might fuse the variable and the indicator into one entity. Qualitative researchers, on the other hand, tend to use the variable-indicator language which causes a translation problem and does not capture research practices in the qualitative culture. The chapter first considers the notion of “membership function,” which is important in the fuzzy-set approach to concepts, before discussing a fundamental principle of semantic transformations in the qualitative culture: the Principle of Unimportant Variation. It also explains the relationship between scale types and membership functions in fuzzy-set analysis.

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