Load prediction method of scraper conveyor based on rough RBF neural network
Accurate prediction of scraper conveyor load is crucial to the cooperative control of shearer and scraper conveyor.The short-term load of scraper conveyor is influenced by uncertain factors such as working face environment and impact load,which is difficult to predict accurately,with the strong nonlinear and non-stationary properties.To solve this problem,a load forecasting method of scraper conveyor based on rough radial basis function neural network was proposed.Firstly,the current denoising model of scraper conveyor was established,and the current component reflecting the real load of scraper conveyor in fully mechanized mining face was obtained.Then,in view of the the increasing training error of neural network prediction model and the low prediction accuracy caused by the large fluctuation of load current of scraper conveyor,a rough radial basis function neural network(RRBFNN)model was proposed by introducing rough neurons representing the fluctuation of load change.Finally,based on RRBFNN,the short-term load forecasting model of scraper conveyor was established and verified by simulation experiments.The results show that the RRBFNN forecasting model of scraper conveyor short-term load is better compared with the conventional RBF model,as the average absolute error(MAE),average absolute percentage error(MAPE)and root mean square error(RMSE)was 26.22%,25.39%and 14.72%lower,which indicates the proposed method can effectively improve the forecasting accuracy of scraper conveyor load.
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