The Experts below are selected from a list of 542238 Experts worldwide ranked by ideXlab platform
Dominique Richon - One of the best experts on this subject based on the ideXlab platform.
-
Extension of an Artificial Neural Network Algorithm for Estimating Sulfur Content of Sour Gases at Elevated Temperatures and Pressures
Industrial and engineering chemistry research, 2010Co-Authors: Medhi Mehrpooya, Amir H. Mohammadi, Dominique RichonAbstract:In this communication, we report all extended artificial neural Network Algorithm to estimate sulfur content of sour/acid gases. The main advantage of this Algorithm is that it eliminates any need for characterization parameters, due to the tendency of sulfurs to react, required in thermodynamic models. To develop this tool, reliable experimental data found in the literature oil sulfur content of various gases are used. To estimate the sulfur content of a gas, the information on temperature, pressure, gravity of acid gas free gas, and the concentrations of hydrogen sulfide and carbon dioxide in the gas are required. The developed Algorithm is then used to predict independent experimental data (not used in its development). It is shown that the artificial neural Network Algorithm can be used as ail efficient tool to estimate sulfur content of various gases.
-
Estimating sulfur content of hydrogen sulfide at elevated temperatures and pressures using an artificial neural Network Algorithm
Industrial and engineering chemistry research, 2008Co-Authors: Amir H. Mohammadi, Dominique RichonAbstract:In this communication, we report an artificial neural Network Algorithm for estimating sulfur content of hydrogen sulfide at elevated temperatures and pressures. This model eliminates any need for characterization parameters, due to the tendency of sulfurs to react, required in thermodynamic models. To develop this Algorithm, reliable experimental data reported in the literature on sulfur content of hydrogen sulfide are used. The developed model is then used to predict independent experimental data (not used in developing the model). It is shown that artificial neural Network Algorithm can be used as an efficient tool to estimate sulfur content of hydrogen sulfide.
Li Li-jun - One of the best experts on this subject based on the ideXlab platform.
-
Neural Network Algorithm for Solving Numerical Integration
Computer Simulation, 2008Co-Authors: Xu Li-ying, Li Li-junAbstract:An Algorithm of neural Network based on the Fourier series for solving numerical integration was proposed.The basic idea is to use the neural Network output to approximate to the integrand.In order to ensure the convergence of Algorithm,the convergence theorem of neural Network Algorithm and the theorem for solving numerical integration were given and proved.This Algorithm was validated by the simulation examples of numerical integration.The results show the presented numerical integration Algorithm has value in engineering practice.
Xu Li-ying - One of the best experts on this subject based on the ideXlab platform.
-
Neural Network Algorithm for Solving Numerical Integration
Computer Simulation, 2008Co-Authors: Xu Li-ying, Li Li-junAbstract:An Algorithm of neural Network based on the Fourier series for solving numerical integration was proposed.The basic idea is to use the neural Network output to approximate to the integrand.In order to ensure the convergence of Algorithm,the convergence theorem of neural Network Algorithm and the theorem for solving numerical integration were given and proved.This Algorithm was validated by the simulation examples of numerical integration.The results show the presented numerical integration Algorithm has value in engineering practice.
Amir H. Mohammadi - One of the best experts on this subject based on the ideXlab platform.
-
Extension of an Artificial Neural Network Algorithm for Estimating Sulfur Content of Sour Gases at Elevated Temperatures and Pressures
Industrial and engineering chemistry research, 2010Co-Authors: Medhi Mehrpooya, Amir H. Mohammadi, Dominique RichonAbstract:In this communication, we report all extended artificial neural Network Algorithm to estimate sulfur content of sour/acid gases. The main advantage of this Algorithm is that it eliminates any need for characterization parameters, due to the tendency of sulfurs to react, required in thermodynamic models. To develop this tool, reliable experimental data found in the literature oil sulfur content of various gases are used. To estimate the sulfur content of a gas, the information on temperature, pressure, gravity of acid gas free gas, and the concentrations of hydrogen sulfide and carbon dioxide in the gas are required. The developed Algorithm is then used to predict independent experimental data (not used in its development). It is shown that the artificial neural Network Algorithm can be used as ail efficient tool to estimate sulfur content of various gases.
-
Estimating sulfur content of hydrogen sulfide at elevated temperatures and pressures using an artificial neural Network Algorithm
Industrial and engineering chemistry research, 2008Co-Authors: Amir H. Mohammadi, Dominique RichonAbstract:In this communication, we report an artificial neural Network Algorithm for estimating sulfur content of hydrogen sulfide at elevated temperatures and pressures. This model eliminates any need for characterization parameters, due to the tendency of sulfurs to react, required in thermodynamic models. To develop this Algorithm, reliable experimental data reported in the literature on sulfur content of hydrogen sulfide are used. The developed model is then used to predict independent experimental data (not used in developing the model). It is shown that artificial neural Network Algorithm can be used as an efficient tool to estimate sulfur content of hydrogen sulfide.
Zhezhao Zeng - One of the best experts on this subject based on the ideXlab platform.
-
A Neural Network Algorithm for Solving Nonlinear Equations
2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing, 2008Co-Authors: Zhezhao Zeng, Dongmei Lin, Lulu ZhengAbstract:In this paper, we proposed a neural Network Algorithm for solving nonlinear equations. The convergence of Algorithm proposed was researched. The convergence theorem provides the theory criterion selecting learning rate of neural Network. The specific examples showed that the proposed method can solve nonlinear equations at a very rapid convergence and very high accuracy with less computation.
-
Optimal Design Study of Hilbert Convertor Based on Neural-Network Algorithm
2008 International Symposium on Intelligent Information Technology Application Workshops, 2008Co-Authors: Zhezhao ZengAbstract:An optimal design approach of Hilbert convertor is researched in detail based on the neural-Network Algorithm. The main idea is to minimize the sum of the square errors between the amplitude-frequency response of the desired Hilbert convertor and that of the designed by training the weight vector of neural-Network, then obtains the impulse response of Hilbert convertor. The convergence theorem of the neural-Network Algorithm is presented and proved, and the optimal design method is introduced by designing two kinds of Hilbert convertors. The results show that the presented optimal design approach of Hilbert convertor is significantly effective.
-
A compensation method of the offset thermal drift of sensor using the neural Network Algorithm
2008 9th International Conference on Signal Processing, 2008Co-Authors: Zhezhao Zeng, Lulu Zheng, Dongmei LingAbstract:The problem of the offset thermal drift affects the measurement accuracy of pressure sensor in practical applications. To solve the problem, we proposed an approach of the non-linear compensation of pressure sensor using the neural Network Algorithm with Chebyshev basis functions. The convergence of the neural Network Algorithm is researched. To validate the validity of the Algorithm, the example of the offset thermal drift compensation of the pressure sensor was given. The simulation result shows that the non-linear compensation approach using the neural Network Algorithm has high compensation accuracy. Therefore, the method of the non-linear compensation proposed is effective.