This paper presents the experimental validation of a numerical model for the prediction of train induced vibrations.
For the prediction of train induced vibrations in the vicinity of planned railroad tracks, a simple power law is proposed.
This paper presents a modular tool for the prediction of train braking performance, with a particular attention to the accurate prediction of stopping distances.
m denotes the total steps to get optimal performance, and (f(x^{(i)}_{text{tr}})) and (f(x^{(i)}_{text{te}})) denote the prediction values of training data and test data, respectively.
From the prediction results of training and testing samples, the squared correlation coefficient (R2) between the observed and predicted values of the proposed model was obtained 0.99 and 0.95, respectively, which shows a high conformity between predicted and actual penetration rate.
Based on mean sum squared error (MSE) and absolute average error in prediction (AEp) of training and testing data, an optimum feed forward neural network based on back propagation was developed with 3 input neurons, 2 hidden layers, 6 neurons in each hidden layer and 1 output neuron 3-6-6-1 3-6-6-1 3-6-6-1 3-6-6-1
The prediction results of training set and independent test set were listed in Table 2.
In this study, Leave-one-out cross-validation (LOOCV) [ 40, 41] was used to investigate the prediction quality of training set.
In DPBM, we simply used a cut-off of the RiskScore in each dysregulated pathways to make a prediction, instead of training a complex classifier such as SVM (Support Vector Machine).
There is currently no evidence that existing genetic tests provide information that is useful regarding either predisposition for a particular sport, prediction of the training response likely to occur to a particular training programme, or predisposition to exercise-related injury.
