Abstract:
In order to effectively improve the accuracyof transportationfunction consumption models, open-pit designer can establish a more detailed material transportation planning model for theproblems that cannot be solved for lack ofestimation method of strip-by-strip transport distance in the annual plan. In this paper a prediction model of multivariate nonlinear haul distance curve trained by extreme learning machine was proposed. The dump strip on transport line designed for year-end dump project location was taken as the training samplesto train prediction model to learn the time varying trait of hual distance and influence factor. Finally, the nonlinear estimation of haul distance expression was used to predict block variable distance. In order to enhance the prediction accuracy of the ELM algorithm,the modified particle swarm optimization algorithm was adopted to build the model of parameters optimization aimed at structural risk minimization and realized the structural risk correction to improve the accuracy of prediction algorithm. The results show that the method of ELM model ultimately determine the number of hidden layer nodes to be 27 through the test of simulation by trial and graphic test.The evaluation indexesof algorithm prediction accuracy (mean square error, goodness of fit, relative error expectation, absolute error expectation, misestimation coefficient, execution efficiency) are 0.006 8, 0.995 3, 0.027%, 0.62,0.03 and 1.49 srespectively.Compared with other prediction model of intelligent algorithm,their absolute error are 0.116 2, 0.094 7, 0.139 1 and the coefficient of miscalculation are 0.230, 0.200, 0.266. In conclusion, the algorithm has better prediction effect obviously.
