The invention provides a dynamics performance parameter optimizing method of a high-speed
train, relates to the field of
parameter design optimizing based on the dynamics
simulation analysis of the high-speed
train, and aims at effectively replacing a dynamics
simulation model of the high-speed
train by a comprehensive target neural
network agent model and combining the
design analysis and the multi-target optimization
algorithm of the high-speed train in the multi-disciplinary field to analyze and optimize the dynamics
simulation approximation model of the high-speed train. The method specifically comprises the steps of building a multi-rigidity dynamics simulation model for the high-speed train; determining related important input / output design spaces; selecting sampling strategy to obtain a
design space sample set suitable for the dynamics performance analysis of the high-speed train; improving the generalization accuracy of the comprehensive target neural network by the bayesian regularization method; adjusting the number of nodes in a
hidden layer to build the comprehensive target neural
network agent network model of which the error is controlled to be within a certain of range; performing multi-target optimization through the intelligent
differential evolution algorithm by using the improved comprehensive target neural
network agent network model to obtain the optimized high-speed train design parameters. The method is mainly applied to the dynamics analysis and design optimization of the high-speed train.