Enhancement of a Neuro-Fuzzy Models Using Ant Colony Optimization for the Prediction Level of CO Pollution

Author(s):  
Z.E. Martinez ◽  
M.A.F. Aceves ◽  
O.R.D. Palma ◽  
O.A. Sotomayor ◽  
H.E. Gorrostieta
2021 ◽  
Author(s):  
Mahdi Danesh ◽  
Sedighe Danesh

Abstract This study employs a new method for regression model prediction in an uncertain environment and presents fuzzy parameter estimation of fuzzy regression models using triangular fuzzy numbers. These estimation methods are obtained by new learning algorithms in which linear programming is used. In this study, the new algorithm is a combination of a fuzzy rule-based system, on the basis of particle swarm optimization (PSO) and ant Colony Optimization AC\({O}_{R}\). In addition, a simulation and a practical example in the field of machining process are applied to indicate the performance of the proposed methods in dealing with problems where the observed variables have the nature of uncertainty and randomness. Finally, the results of the proposed algorithms are evaluated.


2014 ◽  
Vol 04 (04) ◽  
pp. 81-90 ◽  
Author(s):  
Elizabeth Martinez-Zeron ◽  
Marco A. Aceves-Fernandez ◽  
Efren Gorrostieta-Hurtado ◽  
Artemio Sotomayor-Olmedo ◽  
Juan Manuel Ramos-Arreguín

2012 ◽  
Author(s):  
Earth B. Ugat ◽  
Jennifer Joyce M. Montemayor ◽  
Mark Anthony N. Manlimos ◽  
Dante D. Dinawanao

2012 ◽  
Vol 3 (3) ◽  
pp. 122-125
Author(s):  
THAHASSIN C THAHASSIN C ◽  
◽  
A. GEETHA A. GEETHA ◽  
RASEEK C RASEEK C

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