基于神经网络的综采工作面技术经济指标预测

应用人工神经元网络系统理论 ,基于神经网络的自学习方法 ,在对西山煤电集团东曲矿综采工作面大量实际资料进行统计分析的基础上 ,就工作面日进度、日产量、回采工效率、坑木消耗、配件消耗等 1 1项综合技术经济指标进行预测 ,预测结果精确度较高与实际相吻合 .为解决煤矿综采工作面计划、生产、管理的预测决策问题提供了一种新的研究方法.

Prediction of Technical and Economic Norm of the Fully Mechanized Face Based on Neural Network

comprehensive technical and economic norms, such as advance per day of mining face, production per day, productivity of mining face, consumption of pit prop and fittings, have been predicted by applying artificial neural network theory and self-learning based on neural network, on the basis of statistics and analysis of a large quantity of real material about fully mechanized face in Dongqu Colliery of Xishan Bureau of Coal Industry. Its accuracy is high and is fit to the fact. It provides a kind of new researching method for solving prediction and decision of plan, production and management of fully mechanized face in mine.