scholarly journals Rainfall-runoff Modeling Using Dynamic Evolving Neural Fuzzy Inference System with Online Learning

2016 ◽  
Vol 154 ◽  
pp. 1103-1109 ◽  
Author(s):  
Chang Tak Kwin ◽  
Amin Talei ◽  
Sina Alaghmand ◽  
Lloyd H.C. Chua
Keyword(s):  
Online Learning ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Rainfall Runoff ◽  
Inference System ◽  
Runoff Modeling ◽  
Neural Fuzzy

EVENT-BASED RAINFALL-RUNOFF MODELING USING ADAPTIVE NETWORK-BASED FUZZY INFERENCE SYSTEM

2016 ◽  
Vol 78 (9-4) ◽  
Author(s):  
Nadeem Nawaz ◽  
Sobri Harun ◽  
Amin Talei ◽  
Tak Kwin Chang
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Bench Mark ◽  
Physical Parameters ◽  
Rainfall Runoff ◽  
Adaptive Network ◽  
Inference System ◽  
Runoff Modeling ◽  
Hourly Rainfall ◽  
Event Based

Population growth and transformation of agricultural or forest landscapes to built-up areas are the common phenomenon in the fast developing countries. Such changes have significant impact on hydrologic processes in the catchment which in turn may end up with an increase in both magnitude and frequency of floods in urban areas. Therefore, reliable rainfall-runoff models that are able to estimate discharge of a catchment accurately are in need. To date, several physically-based models are developed to capture the rainfall-runoff process; however, they require significant number of parameters which could be difficult to be measured or estimated. Beside these models, the artificial intelligence techniques have shown their ability to identify a direct mapping between inputs and outputs with less number of physical parameters. Adaptive network-based fuzzy inference system (ANFIS) is one of the well-practiced techniques in hydrological time series modeling. The aim of this study was to check the capability of ANFIS in event-based rainfall runoff modeling for a tropical catchment. A total of 70 rainfall-runoff events were extracted from twelve years hourly rainfall and runoff data of Semenyih River catchment where 50 of them were chosen for training and the remaining 20 for testing. An input selection method based on correlation analysis and mutual information was developed to identify the proper input combinations of rainfall and discharge antecedents. The results obtained by ANFIS model were then compared with an autoregressive model with exogenous inputs (ARX) as a bench mark. Results showed that ANFIS outperforms ARX model and has capabilities to be used as a reliable rainfall-runoff modeling tool.

scholarly journals Rainfall-Runoff Modeling using Artificial Neural Network and Neuro-Fuzzy Inference System in Kasilian Watershed

2016 ◽  
Vol 7 (13) ◽  
pp. 137-128 ◽  
Author(s):  
نوید دهقانی ◽  
مهدی وفاخواه ◽  
عبدالرضا بهره مند ◽  
◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Rainfall Runoff ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Runoff Modeling ◽  
Artificial Neural

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.

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