Flood hydrograph modeling using artificial neural network and adaptive neuro-fuzzy inference system based on rainfall components

2021 ◽  
Vol 14 (5) ◽  
Author(s):  
Saeid Janizadeh ◽  
Mehdi Vafakhah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Flood Hydrograph ◽  
Artificial Neural

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

scholarly journals Estimation of the Solid Circulation Rate in Circulating Fluidized Bed System Using Adaptive Neuro-Fuzzy Algorithm

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 211
Author(s):  
Aamer Bilal Asghar ◽  
Saad Farooq ◽  
Muhammad Shahzad Khurram ◽  
Mujtaba Hussain Jaffery ◽  
Krzysztof Ejsmont
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Analysis ◽  
Fuzzy Inference System ◽  
Circulating Fluidized Bed ◽  
Fuzzy Inference ◽  
Mean Square ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Artificial Neural

Circulating Fluidized Bed gasifiers are widely used in industry to convert solid fuel into liquid fuel. The Artificial Neural Network and neuro-fuzzy algorithm have immense potential to improve the efficiency of the gasifier. The main focus of this article is to implement the Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System modeling approach to estimate solid circulation rate at high pressure in the Circulating Fluidized Bed gasifier. The experimental data is obtained on a laboratory scale prototype in the Chemical Engineering laboratory at COMSATS University Islamabad. The Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System use four input features—pressure, single mean diameter, total valve opening and riser dp—and one output feature mass flow rate with multiple neurons in the hidden layers to estimate the flow of solid particles in the riser. Both Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System model worked on 217 data samples and output results are compared based on their Mean Square Error, Regression analysis, Mean Absolute Error and Mean Absolute Percentage Error. The experimental results show the effectiveness of Adaptive Neuro-Fuzzy Inference System (Mean Square Error is 0.0519 and Regression analysis R2=1.0000), as it outperformed Artificial Neural Network in terms of accuracy (Mean Square Error is 1.0677 and Regression analysis R2=0.9806).

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