Study on correlation analysis of ride comfort indices based on genetic neural networks

To solve the comparison and transformation problem of different ride comfort indexes, a correlation analysis approach based on genetic neural networks was proposed. Firstly, vibration accelerations were obtained by track spectrum and ADAMS/Rail dynamic simulation software. Secondly, how to calculate ride comfort standard was illustrated using UIC513 standard. Thirdly, the correlation models of ride comfort indices were constructed using neural networks. The structures of the neural networks were determined empirically, and the parameters of the neural networks were trained by combination of genetic algorithm and Levenberg-Marquardt algorithm. The experiment results show that there are high correlations between ride comfort indices, and that the genetic neural networks can convert one index to any other index successfully and precisely.