scholarly journals Comparison of multi-gene genetic programming and dynamic evolving neural-fuzzy inference system in modeling pan evaporation

2017 ◽  
Vol 49 (4) ◽  
pp. 1221-1233 ◽  
Author(s):  
Okan Eray ◽  
Cihan Mert ◽  
Ozgur Kisi
Keyword(s):  
Genetic Programming ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Empirical Method ◽  
Data Driven ◽  
Maximum Temperature ◽  
Climatic Data ◽  
Pan Evaporation ◽  
Inference System ◽  
Neural Fuzzy

AbstractAccurately modeling pan evaporation is important in water resources planning and management and also in environmental engineering. This study compares the accuracy of two new data-driven methods, multi-gene genetic programming (MGGP) approach and dynamic evolving neural-fuzzy inference system (DENFIS), in modeling monthly pan evaporation. The climatic data, namely, minimum temperature, maximum temperature, solar radiation, relative humidity, wind speed, and pan evaporation, obtained from Antakya and Antalya stations, Mediterranean Region of Turkey were utilized in the study. The MGGP and DENFIS methods were also compared with genetic programming (GP) and calibrated version of Hargreaves Samani (CHS) empirical method. For Antakya station, GP had slightly better accuracy than the MGGP and DENFIS models and all the data-driven models performed were superior to the CHS while the DENFIS provided better performance than the other models in modeling pan evaporation at Antalya station. The effect of periodicity input to the models' accuracy was also investigated and it was found that adding periodicity significantly increased the accuracy of MGGP and DENFIS models.

scholarly journals A New Method for Evaporation Modeling: Dynamic Evolving Neural-Fuzzy Inference System

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Ozgur Kisi ◽  
Iman Mansouri ◽  
Jong Wan Hu
Keyword(s):  
Fuzzy Inference System ◽  
Cross Validation ◽  
Fuzzy Inference ◽  
New Method ◽  
Mean Square ◽  
Pan Evaporation ◽  
Anfis Model ◽  
Inference System ◽  
Absolute Relative Error ◽  
Neural Fuzzy

Evaporation estimation is very essential for planning and development of water resources. The study investigates the ability of new method, dynamic evolving neural-fuzzy inference system (DENFIS), in modeling monthly pan evaporation. Monthly maximum and minimum temperatures, solar radiation, wind speed, and relative humidity data obtained from two stations located in Turkey are used as inputs to the models. The results of DENFIS method were compared with the classical adaptive neural-fuzzy inference system (ANFIS) by using root mean square error (RMSE), mean absolute relative error (MARE), and Nash-Sutcliffe Coefficient (NS) statistics. Cross validation was applied for better comparison of the models. The results indicated that DENFIS models increased the accuracy of ANFIS models to some extent. RMSE, MARE, and NS of the ANFIS model were increased by 11.13, 11.45, and 6.83% for the Antalya station and 20.11, 12.94%, and 8.29% for the Antakya station using DENFIS.

scholarly journals A NEURAL FUZZY INFERENCE BASED ADAPTIVE CONTROLLER FOR NONHOLONOMIC ROBOTS

2011 ◽  
pp. 56-65
Author(s):  
Ting Wang ◽  
Fabien Gautero ◽  
Christophe Sabourin ◽  
Kurosh Madani
Keyword(s):  
Control Strategy ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Adaptive Controller ◽  
Reference Trajectory ◽  
Inference System ◽  
Nonholonomic Robots ◽  
Real Robot ◽  
Neuro Controller ◽  
Neural Fuzzy

In this paper, we propose a control strategy for a nonholonomic robot which is based on an Adaptive Neural Fuzzy Inference System. The neuro-controller makes it possible the robot track a desired reference trajectory. After a short reminder about Adaptive Neural Fuzzy Inference System, we describe the control strategy which is used on our virtual nonholonomic robot. And finally, we give the simulations’ results where the robot have to pass into a narrow path as well as the first validation results concerning the implementation of the proposed concepts on real robot.

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>

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