Soil parameter identification using a genetic algorithm

2008 ◽  
Vol 32 (2) ◽  
pp. 189-213 ◽  
Author(s):  
S. Levasseur ◽  
Y. Malécot ◽  
M. Boulon ◽  
E. Flavigny
Keyword(s):  
Genetic Algorithm ◽  
Parameter Identification ◽  
Soil Parameter

Carbon Brake Friction Model Parameter Identification Using Genetic Algorithms

1995 ◽  
Author(s):  
Roger C. von Doenhoff ◽  
Robert J. Streifel ◽  
Robert J. Marks
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Surface Temperature ◽  
Parameter Identification ◽  
Friction Model ◽  
Carbon Surface ◽  
Model Parameters ◽  
Model Parameter ◽  
Brake Pressure ◽  
Model Parameter Identification

Abstract A model of the friction characteristics of carbon brakes is proposed to aid in the understanding of the causes of brake vibration. The model parameters are determined by a genetic algorithm in an attempt to identify differences in friction properties between brake applications during which vibration occurs and those during which there is no vibration. The model computes the brake torque as a function of wheelspeed, brake pressure, and the carbon surface temperature. The surface temperature is computed using a five node temperature model. The genetic algorithm chooses the model parameters to minimize the error between the model output and the torque measured during a dynamometer test. The basics of genetic algorithms and results of the model parameter identification process are presented.

Soil parameter identification for excavations: A falsification approach

2018 ◽  
pp. 1180-1188
Author(s):  
Wang Ze-Zhou ◽  
Goh Siang Huat ◽  
SacKoh Chanhin Ghee ◽  
Ian F.C. Smith
Keyword(s):  
Parameter Identification ◽  
Soil Parameter

scholarly journals Parameter identification of Bouc-Wen model for Magnetorheological (MR) fluid Damper by a Novel Genetic Algorithm

2020 ◽  
Vol 12 (8) ◽  
pp. 168781402095054
Author(s):  
Birhan Abebaw Negash ◽  
Wonhee You ◽  
Jinho Lee ◽  
Kwansup Lee
Keyword(s):  
Genetic Algorithm ◽  
Parameter Identification ◽  
Mr Damper ◽  
Absolute Error ◽  
Mr Fluid ◽  
Fitness Evaluation ◽  
Damper Control ◽  
Fluid Dampers ◽  
Mr Fluid Damper ◽  
Crossover And Mutation

In this research, novel genetic algorithm (nGA) is proposed for Bouc-Wen modle parameters esstimation for magnetorheological (MR) fluid dampers. The optimization efficiency is improved by modifying the crossover and mutation steps of a GA. In the crossover stage, the probability of reproducing offspring from the same parent (same mother and father chromosome) is done to be zero, which may happen in the standard GA, and the probability of a chromosome to be selected for mating is based on error rank weighting of the chromosomes. Additional fitness evaluation of chromosomes will take place in between the crossover and mutation steps to save the best chromosome found so far, which is not implemented in the standard genetic algorithm (GA). The model is validated by comparing its simulation output force ( Fsim) with experimentally generated MR damper force ( Fexp). The mean absolute error, standard deviation and number of generations for convergence are taken as a criterias for performance evaluation. With these ctriterias, the proposed novel GA outperform better than the other researches. The accuracy is improved by 46.67% compared to standard GA. The proposed novel GA for Bouc-Wen model parameter identification can be used for any MR damper control system with better accuracy.

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