Ranking fuzzy numbers based on relative positions and shape characteristics

2022 ◽  
Vol 191 ◽  
pp. 116312
Author(s):  
Nguyen Van Hop
Keyword(s):  
Fuzzy Numbers ◽  
Shape Characteristics

Improved five-factor Altman evaluation model credit about the enterprise with economic indicators as fuzzy numbers

2020 ◽  
Vol 7 (1) ◽  
pp. 72-83
Author(s):  
Alevtina Yu. Shatalova ◽  
◽  
Igor V. Shevchenko ◽  
Boureima Bamadio ◽  
Konstantin A. Lebedev ◽  
...  
Keyword(s):  
Fuzzy Numbers ◽  
Evaluation Model ◽  
Economic Indicators

A study on generalized quadrilateral fuzzy numbers

2020 ◽  
Vol S (1) ◽  
pp. 295-298
Author(s):  
Stephen Dinagar D. ◽  
Manvizhi M.
Keyword(s):  
Fuzzy Numbers

Totally Symmetric Type-2 Triangular Fuzzy Numbers

2018 ◽  
Vol 9 (11) ◽  
pp. 1717-1727
Author(s):  
Ajay Minj ◽  
Pathinathan T.
Keyword(s):  
Fuzzy Numbers ◽  
Triangular Fuzzy Numbers

Genetic,gonadal development and shape characteristics researches of gynogenetic orange ornamental carp(Cyprinus carpio L.)

2013 ◽  
Vol 37 (3) ◽  
pp. 390 ◽  
Author(s):  
Qizhi LIU ◽  
Jun XIAO ◽  
Kaikun LUO ◽  
Yong ZHANG ◽  
Yuequn WANG
Keyword(s):  
Cyprinus Carpio ◽  
Gonadal Development ◽  
Carp Cyprinus Carpio ◽  
Shape Characteristics

scholarly journals Modification of the Logistic Map Using Fuzzy Numbers with Application to Pseudorandom Number Generation and Image Encryption

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 474 ◽  
Author(s):  
Lazaros Moysis ◽  
Christos Volos ◽  
Sajad Jafari ◽  
Jesus M. Munoz-Pacheco ◽  
Jacques Kengne ◽  
...  
Keyword(s):  
Lyapunov Exponent ◽  
Image Encryption ◽  
Fuzzy Numbers ◽  
Statistical Tests ◽  
Logistic Map ◽  
Simple Rule ◽  
Pseudorandom Number ◽  
Bifurcation Diagrams ◽  
Triangular Numbers ◽  
Pseudorandom Number Generation

A modification of the classic logistic map is proposed, using fuzzy triangular numbers. The resulting map is analysed through its Lyapunov exponent (LE) and bifurcation diagrams. It shows higher complexity compared to the classic logistic map and showcases phenomena, like antimonotonicity and crisis. The map is then applied to the problem of pseudo random bit generation, using a simple rule to generate the bit sequence. The resulting random bit generator (RBG) successfully passes the National Institute of Standards and Technology (NIST) statistical tests, and it is then successfully applied to the problem of image encryption.

Image Micro-pattern Analysis Using Fuzzy Numbers

2012 ◽  
Author(s):  
Raissa T. Vieira ◽  
Carlos Eduardo de Oliveira Chierici ◽  
Carolina T. Ferraz ◽  
Adilson Gonzaga
Keyword(s):  
Fuzzy Numbers ◽  
Pattern Analysis ◽  
Micro Pattern

scholarly journals On Present Value Evaluation under the Impact of Behavioural Factors Using Oriented Fuzzy Numbers

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 468
Author(s):  
Krzysztof Piasecki ◽  
Anna Łyczkowska-Hanćkowiak
Keyword(s):  
Fuzzy Numbers ◽  
Asset Price ◽  
Financial Knowledge ◽  
Present Value ◽  
Market Knowledge ◽  
Value Evaluation ◽  
Starting Point ◽  
Behavioural Factors ◽  
Objective Financial Knowledge ◽  
The Impact

In general, the present value (PV) concept is ambiguous. Therefore, behavioural factors may influence on the PV evaluation. The main aim of our paper is to propose some method of soft computing PV evaluated under the impact of behavioural factors. The starting point for our discussion is the notion of the Behavioural PV (BPV) defined as an imprecisely real-valued function of distinguished variables which can be evaluated using objective financial knowledge or subjective behavioural premises. In our paper, a BPV is supplemented with a forecast of the asset price closest to changes. Such BPV is called the oriented BPV (O-BPV). We propose to evaluate an O-BPV by oriented fuzzy numbers which are more useful for portfolio analysis than fuzzy numbers. This fact determines the significance of the research described in this article. O-BPV may be applied as input signal for systems supporting invest-making. We consider here six cases of O-BPV: overvalued asset with the prediction of a rise in its price, overvalued asset with the prediction of a fall in its price, undervalued asset with the prediction of a rise in its price, undervalued asset with the prediction of a fall in its price, fully valued asset with the prediction of a rise in its rice and fully valued asset with the prediction of a fall in its rice. All our considerations are illustrated by numerical examples. Presented examples show the way in which we transform superposition of objective market knowledge and subjective investment opinion into simple return rate.

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