scholarly journals Application of ANNs approach for solving fully fuzzy polynomials system

2017 ◽  
Vol 2017 (3) ◽  
pp. 132-142
Author(s):  
R. Novin ◽  
M. A. Fariborzi Araghi ◽  
M. Amirfakhrian
Keyword(s):  
Fuzzy Polynomials

Numerical approximation of fuzzy functions by fuzzy polynomials

2006 ◽  
Vol 174 (2) ◽  
pp. 1001-1006 ◽  
Author(s):  
S. Abbasbandy ◽  
M. Amirfakhrian
Keyword(s):  
Numerical Approximation ◽  
Fuzzy Polynomials

MULTIVARIATE NON-LINEAR FUZZY REGRESSION: AN EVOLUTIONARY ALGORITHM APPROACH

1999 ◽  
Vol 07 (02) ◽  
pp. 83-98 ◽  
Author(s):  
James J. Buckley ◽  
Thomas Feuring ◽  
Yoichi Hayashi
Keyword(s):  
Evolutionary Algorithm ◽  
Fuzzy Numbers ◽  
Fuzzy Regression ◽  
Important Class ◽  
Fuzzy Function ◽  
Non Linear ◽  
Fuzzy Polynomials ◽  
The One

We first argue that a very important class of fuzzy functions in multivariate non-linear fuzzy regression is the multivariate fuzzy polynomials. Given some data, generated by a multivariate fuzzy function, our evolutionary algorithm searches our library of multivariate fuzzy polynomials for the one that best fits this data. Tests of our multivariate non-linear fuzzy regression package are given when the multivariate fuzzy function is a multivariate fuzzy polynomial and all the fuzzy numbers are non-negative.

scholarly journals Particle Swarm Optimization for Solving a Class of Type-1 and Type-2 Fuzzy Nonlinear Equations

2018 ◽  
Vol 8 (2) ◽  
pp. 103-110 ◽  
Author(s):  
Sheriff Sadiqbatcha ◽  
Saeed Jafarzadeh ◽  
Yiannis Ampatzidis
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Sets ◽  
Nonlinear Equations ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Fuzzy Variables ◽  
Modified Particle Swarm Optimization ◽  
Fuzzy Polynomials ◽  
Exponential Equations

Abstract This paper proposes a modified particle swarm optimization (PSO) algorithm that can be used to solve a variety of fuzzy nonlinear equations, i.e. fuzzy polynomials and exponential equations. Fuzzy nonlinear equations are reduced to a number of interval nonlinear equations using alpha cuts. These equations are then sequentially solved using the proposed methodology. Finally, the membership functions of the fuzzy solutions are constructed using the interval results at each alpha cut. Unlike existing methods, the proposed algorithm does not impose any restriction on the fuzzy variables in the problem. It is designed to work for equations containing both positive and negative fuzzy sets and even for the cases when the support of the fuzzy sets extends across 0, which is a particularly problematic case.

scholarly journals Solving fully fuzzy polynomials using feed-back neural networks

2014 ◽  
Vol 92 (4) ◽  
pp. 742-755 ◽  
Author(s):  
Ahmad Jafarian ◽  
Raheleh Jafari ◽  
Alireza Khalili Golmankhaneh ◽  
Dumitru Baleanu
Keyword(s):  
Neural Networks ◽  
Fuzzy Polynomials

Solutions of interval type-2 fuzzy polynomials using a new ranking method

2015 ◽  
Author(s):  
Nurhakimah Ab. Rahman ◽  
Lazim Abdullah ◽  
Ahmad Termimi Ab. Ghani ◽  
Noor ’Ani Ahmad
Keyword(s):  
Ranking Method ◽  
Fuzzy Polynomials ◽  
Interval Type
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