Selection of neural network model in soot-blowing optimization system for coal-fired utility boilers

2015 ◽  
pp. 119-122
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Optimization System ◽  
Utility Boilers ◽  
Selection Of

Characterization of hidden rules linking symptoms and selection of acupoint using an artificial neural network model

2018 ◽  
Vol 13 (1) ◽  
pp. 112-120 ◽  
Author(s):  
Won-Mo Jung ◽  
In-Soo Park ◽  
Ye-Seul Lee ◽  
Chang-Eop Kim ◽  
Hyangsook Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Artificial Neural ◽  
Selection Of

scholarly journals Complexity selection of a neural network model for karst flood forecasting: The case of the Lez Basin (southern France)

2011 ◽  
Vol 403 (3-4) ◽  
pp. 367-380 ◽  
Author(s):  
Line Kong A Siou ◽  
Anne Johannet ◽  
Valérie Borrell ◽  
Séverin Pistre
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Flood Forecasting ◽  
Southern France ◽  
Selection Of

scholarly journals SELECTION OF INPUT VARIABLES OF NONLINEAR AUTOREGRESSIVE NEURAL NETWORK MODEL FOR TIME SERIES DATA FORECASTING

2020 ◽  
Vol 13 (2) ◽  
pp. 116-124
Author(s):  
Hermansah Hermansah ◽  
Dedi Rosadi ◽  
Abdurakhman Abdurakhman ◽  
Herni Utami
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
Time Series Data ◽  
Series Data ◽  
Input Variables ◽  
Nonlinear Autoregressive ◽  
Selection Of ◽  
Autoregressive Neural Network

NARNN is a type of ANN model consisting of a limited number of parameters and widely used for various applications. This study aims to determine the appropriate NARNN model, for the selection of input variables of nonlinear autoregressive neural network model for time series data forecasting, using the stepwise method. Furthermore, the study determines the optimal number of neurons in the hidden layer, using a trial and error method for some architecture. The NARNN model is combined in three parts, namely the learning method, the activation function, and the ensemble operator, to get the best single model. Its application in this study was conducted on real data, such as the interest rate of Bank Indonesia. The comparison results of MASE, RMSE, and MAPE values with ARIMA and Exponential Smoothing models shows that the NARNN is the best model used to effectively improve forecasting accuracy.

Sign in / Sign up

Export Citation Format

Share Document