scholarly journals Adaptive neuro-fuzzy inference system (ANFIS) and response surface methodology (RSM) prediction of biodiesel dynamic viscosity at 313 K

2017 ◽  
Vol 120 ◽  
pp. 521-528 ◽  
Author(s):  
Youssef Kassem ◽  
Hüseyin Çamur ◽  
Engin Esenel
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Dynamic Viscosity ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

scholarly journals RSM And ANFIS Based Parameters Prediction of Robot Using GRA

2018 ◽  
Vol 7 (4.36) ◽  
pp. 604
Author(s):  
P. Gopu ◽  
M. Dev Anand ◽  
. .
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Robot Arm ◽  
Inference System ◽  
Performance Variation ◽  
Neuro Fuzzy ◽  
Objective Parameter ◽  
Performance Uncertainty

Ability of robot arm manipulation must be highly accurate and repeatable one. Performance uncertainty is causes by some noise factor. The effects of these factors were model to reduce the uncertainty of the robotic arm performance. In this paper highlights the prediction of output parameters robot cell data like X, Y and Z axis through Response Surface Methodology (RSM) and Adaptive Neuro Fuzzy Inference System (ANFIS) for reduce the performance variation of the robot. The input kinematic parameters like θ1, θ2, θ3, θ4, θ5 has been considered and the output multi objective parameters X, Y and Z axis has been converted in to single objective parameter. The graph which plots between parameters and the output response indicates the influence of the every single parameter for the performance output contribution. From the simulated values of Response Surface Methodology and Adaptive Neuro Fuzzy Inference System, the percentage of error obtained in Adaptive Neuro Fuzzy Inference System has minimum one when compared with Response Surface Methodology of prediction.  

Sign in / Sign up

Export Citation Format

Share Document