Research on the algorithm of shearer power prediction based on extreme learning machine

Department

Mechanical Engineering

Document Type

Article

Publication Title

Meitan Xuebao/Journal of the China Coal Society

ISSN

0253-9993

Volume

41

Issue

3

DOI

10.13225/j.cnki.jccs.2015.0928

First Page

794

Last Page

800

Publication Date

3-1-2016

Abstract

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.

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