Figure7

Suspension parameter identification method for rail transit vehicles using an AO-GRBF surrogate model and non-dominated sorting genetic algorithm

Figure 7. Distributions of the identified suspension parameters $$ x_1 $$$$ x_6 $$ across 20 independent trials (units: N/m). Each violin plot illustrates the range, density, and clustering of parameter values, indicating consistent convergence across different surrogate retraining instances.

Intelligence & Robotics
ISSN 2770-3541 (Online)

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