scholarly journals Erratum to: River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia

2012 ◽  
Vol 26 (7) ◽  
pp. 2123-2123
Author(s):  
M. R. Mustafa ◽  
R. B. Rezaur ◽  
S. Saiedi ◽  
M. H. Isa
Keyword(s):  
Neural Network ◽  
Suspended Sediment ◽  
Multilayer Perceptron ◽  
Training Algorithms ◽  
Neural Network Training ◽  
Network Training

Crop parameter estimation of Lady finger by using different neural network training algorithms

2010 ◽  
Vol 36 (1) ◽  
pp. 71-77 ◽  
Author(s):  
Abhishek Pandey ◽  
J. K. Srivastava ◽  
N. S. Rajput ◽  
R. Prasad
Keyword(s):  
Neural Network ◽  
Parameter Estimation ◽  
Training Algorithms ◽  
Neural Network Training ◽  
Network Training

scholarly journals Performance Analysis of Traffic Congestion Using Designated Neural Network Training Algorithms

2021 ◽  
Vol 20 ◽  
pp. 23-33
Author(s):  
Ituabhor Odesanya ◽  
Joseph Femi Odesanya
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Bayesian Regularization ◽  
Training Algorithms ◽  
Neural Network Training ◽  
Flow Data ◽  
Network Training ◽  
Ondo State ◽  
Coefficient Of Regression

A lot of neural network training algorithms on prediction exist and these algorithms are being used by researchers to solve evaluation, forecasting, clustering, function approximation etc. problems in traffic volume congestion. This study is aimed at analysing the performance of traffic congestion using some designated neural network training algorithms on traffic flow in some selected corridors within Akure, Ondo state, Nigeria. The selected corridors were Oba Adesida road, Oyemekun road and Oke Ijebu road all in Akure. The traffic flow data were collected manually with the help of field observers who monitored and record traffic movement along the corridors. To accomplish this, three common training algorithms were selected to train the traffic flow data. The data were trained using Bayesian Regularization (BR), Scaled Conjugate Gradient (SCG) and Levenberg-Marquardt (LM) training algorithms. The outputs/performances of these training functions were evaluated by using the Mean Square Error (MSE) and Coefficient of Regression (R) to find the best training algorithms. The results show that, the Bayesian regularization algorithm, performs better with MSE of 2.37e-13 and R of 0.9999 than SCG and LM algorithms.

A comparative case study of neural network training by using frame-level cost functions for automatic speech recognition purposes in Spanish

2020 ◽  
Vol 79 (27-28) ◽  
pp. 19669-19715
Author(s):  
Aldonso Becerra ◽  
J. Ismael de la Rosa ◽  
Efrén González ◽  
A. David Pedroza ◽  
N. Iracemi Escalante ◽  
...  
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Cost Functions ◽  
Comparative Case Study ◽  
Neural Network Training ◽  
Network Training
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