A New Fuzzy Inference System Based on Artificial Neural Network and Its Applications

1999 ◽  
pp. 75-94
Author(s):  
J. Łęski ◽  
E. Czogała
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Artificial Neural

Robust extraction of fuzzy rules with artificial neural network based on fuzzy inference system

2012 ◽  
Vol 6 (1) ◽  
pp. 77
Author(s):  
Robert Czabanski ◽  
Michal Jezewski ◽  
Janusz Jezewski ◽  
Janusz Wrobel ◽  
Krzysztof Horoba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Fuzzy Rules ◽  
Inference System ◽  
Artificial Neural

scholarly journals Inverse Kinematic Solution of 5R Manipulator using ANN and ANFIS

2015 ◽  
Vol 4 (2) ◽  
pp. 109
Author(s):  
Panchand Jha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Inference System ◽  
Inverse Kinematics ◽  
Fuzzy Inference ◽  
Robot Manipulator ◽  
Ann Model ◽  
Inference System ◽  
Artificial Neural ◽  
Neural Fuzzy

<span>Inverse kinematics of manipulator comprises the computation required to find the joint angles for a given Cartesian position and orientation of the end effector. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Artificial neural network and adaptive neural fuzzy inference system techniques can be gainfully used to yield the desired results. This paper proposes structured artificial neural network (ANN) model and adaptive neural fuzzy inference system (ANFIS) to find the inverse kinematics solution of robot manipulator. The ANN model used is a multi-layered perceptron Neural Network (MLPNN). Wherein, gradient descent type of learning rules is applied. An attempt has been made to find the best ANN configuration for the problem. It is found that ANFIS gives better result and minimum error as compared to ANN.</span>

Modeling of rheological behavior of honey using genetic algorithm–artificial neural network and adaptive neuro-fuzzy inference system

2015 ◽  
Vol 9 ◽  
pp. 60-67 ◽  
Author(s):  
Marziyeh Ramzi ◽  
Mahdi Kashaninejad ◽  
Fakhreddin Salehi ◽  
Ali Reza Sadeghi Mahoonak ◽  
Seyed Mohammad Ali Razavi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Rheological Behavior ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Artificial Neural
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