TY - JOUR
T1 - Optimization-Based Parameter Identification for Coupled Biodynamic Model of Seated Posture under Vibration
AU - Yang, Yanwen
AU - Zhao, Qinghai
AU - Yang, James
N1 - Publisher Copyright:
© 2022 SAE International.
PY - 2022/2/4
Y1 - 2022/2/4
N2 - We recently developed a three-direction (vertical, longitudinal, and lateral) coupled biodynamic model of seated posture under vibration. However, in that study we only tested one algorithm to identify the model parameters. This article investigates four different optimization solvers in Matlab®, i.e., particle swarm optimization (particleswarm), particle swarm and local optimization method (fmincon), genetic algorithm (ga) and local optimization method (fmincon), and local optimization method (fmincon) to identify coupled biodynamic model parameters. Based on the obtained parameters, it further compares experimental and simulation results to determine the best optimization solver in terms of the root mean square error (RMSE), linear regression (R 2), goodness of fit (ϵ), and Central Processing Unit (CPU) time. The results show that particle swarm optimization is the best one for identifying the biodynamic model's parameters.
AB - We recently developed a three-direction (vertical, longitudinal, and lateral) coupled biodynamic model of seated posture under vibration. However, in that study we only tested one algorithm to identify the model parameters. This article investigates four different optimization solvers in Matlab®, i.e., particle swarm optimization (particleswarm), particle swarm and local optimization method (fmincon), genetic algorithm (ga) and local optimization method (fmincon), and local optimization method (fmincon) to identify coupled biodynamic model parameters. Based on the obtained parameters, it further compares experimental and simulation results to determine the best optimization solver in terms of the root mean square error (RMSE), linear regression (R 2), goodness of fit (ϵ), and Central Processing Unit (CPU) time. The results show that particle swarm optimization is the best one for identifying the biodynamic model's parameters.
KW - Biodynamic model
KW - Multi-objective optimization
KW - Parameter identification
KW - Seated human body
UR - http://www.scopus.com/inward/record.url?scp=85125590533&partnerID=8YFLogxK
U2 - 10.4271/10-06-02-0011
DO - 10.4271/10-06-02-0011
M3 - Article
AN - SCOPUS:85125590533
SN - 2380-2162
VL - 6
JO - SAE International Journal of Vehicle Dynamics, Stability, and NVH
JF - SAE International Journal of Vehicle Dynamics, Stability, and NVH
IS - 2
ER -