Research on the algorithm of shearer power prediction based on extreme learning machine
Meitan Xuebao/Journal of the China Coal Society
In order to not excessively rely on domain experts and the inherit experimental knowledge of the experts, this paper presents a prediction model of shearer power based on Extreme Learning Machine (ELM), and combined with the mapping relationship between condition attributes and decision attributes for the overall design technical parameters determination in the design process. The model is built and optimized by identifying the optimum number of neurons on hidden layer with genetic algorithm, determining the excitation function with progressive comparison, randomly generating input weights and hidden elements bias and calculating hidden layer nodes output matrix, hidden layer and output layer connection weights. The model could output the predictive values of shearer power according to different original conditions input by users. Real design data were adopted to do algorithm analysis and contrast experiment with the reasoning model based on Support Vector Machine (SVM). The results show that ELM model can be used to complete a single power prediction in 600 ms. The average relative error of predicted values and real value is within 2.5%. The prediction accuracy of the proposed model is better than that of SVM model and it is of an apparent advantage over SVM model in learning speed. The reasoning efficiency has been improved significantly.
Research on the algorithm of shearer power prediction based on extreme learning machine.
Meitan Xuebao/Journal of the China Coal Society, 41(3), 794–800.