Modeling and Parameter Identification of FCCU Regenerator with Modified Levenberg-Marquardt Algorithm

Author(s):  
Yi Zheng ◽  
Shaoyuan Li ◽  
Wenjun Zhu
Solar Energy ◽  
2014 ◽  
Vol 110 ◽  
pp. 781-788 ◽  
Author(s):  
Fayrouz Dkhichi ◽  
Benyounes Oukarfi ◽  
Abderrahim Fakkar ◽  
Noureddine Belbounaguia

2013 ◽  
Vol 210 ◽  
pp. 265-270 ◽  
Author(s):  
Anna Obrączka ◽  
Wojciech Mitkowski

In this paper the parameter identification methods for nonlinear models were compared for fractional, partial differential equation. The compared three methods are: the Levenberg-Marquardt algorithm, the Gauss-Newton algorithm and Nelder-Mead Simplex method. The series of numerical experiments were performed to test their robustness and calculation speed. The result of this tests were presented and described.


2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.


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