Optimal parameter estimation for Muskingum model based on Gray-encoded accelerating genetic algorithm

2007 ◽  
Vol 12 (5) ◽  
pp. 849-858 ◽  
Author(s):  
Jianjun Chen ◽  
Xiaohua Yang
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Optimal Parameter ◽  
Model Based ◽  
Muskingum Model ◽  
Optimal Parameter Estimation ◽  
Accelerating Genetic Algorithm

scholarly journals Optimal Parameter Estimation for Muskingum Model Using a CSS-PSO Method

2013 ◽  
Vol 5 ◽  
pp. 480954 ◽  
Author(s):  
S. Talatahari ◽  
R. Sheikholeslami ◽  
B. Farahmand Azar ◽  
H. Daneshpajouh
Keyword(s):  
Parameter Estimation ◽  
Optimal Parameter ◽  
Muskingum Model ◽  
Optimal Parameter Estimation

scholarly journals Optimal parameter estimation for a DC motor using genetic algorithm

2020 ◽  
Vol 11 (2) ◽  
pp. 1047 ◽  
Author(s):  
Mohammad Soleimani Amiri ◽  
Mohd Faisal Ibrahim ◽  
Rizauddin Ramli
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Parameter Estimation ◽  
Numerical Analysis ◽  
Optimal Parameter ◽  
Dc Motor ◽  
Linear Features ◽  
Estimated Parameters ◽  
Optimal Parameter Estimation ◽  
Non Linear

Estimating the parameters of a geared DC motor is crucial in terms of its non-linear features. In this paper, parameters of a geared DC motor are estimated genetically. Mathematical model of the DC motor is determined by Kirchhoff’s law and dynamic model of its shafts and gearbox. Parameters of the geared DC motor are initially estimated by MATLAB/SIMULINK. The estimated parameters are defined as initial values for Genetic Algorithm (GA) to minimize the error of the simulated and actual angular trajectory captured by an encoder. The optimal estimated model of the geared DC motor is validated by different voltages as the input of the actual DC motor and its mathematical model. The results and numerical analysis illustrate it can be ascertained that GA is appropriate to estimate the parameters of platforms with non linear characteristics.

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