The Grey Markov Chain Model Based on Sliding Window in Vertical Steering Locus Forecasting for Shearers

Chen Xiang, Fang Peng, Chen Fenglei, Wang Song
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Abstract

Aiming at the current situation that the coal-rock interface identification in the coal mining process is difficult to realize, the solution of predictive algorithm to realize the locus prediction of the shearer is proposed, so that the intelligent control of the shearer is realized. The gray Markov chain predictive model based on sliding window is proposed due to the small number of data samples in the process of locus prediction of the shearer. The model has the characteristics of relatively simple algorithm, ability to respond to random events, high prediction accuracy and fast calculation speed. Through the simulation experiment, the sliding window width was determined and the predictive algorithm was verified at the same time. The simulation experiment results indicated that the predictive algorithm has fast response speed and high prediction accuracy, which is suitable for practical applications.
基于滑动窗口的灰色马尔可夫链模型在采煤机垂直转向轨迹预测中的应用
针对采煤过程中煤岩界面识别难以实现的现状,提出了利用预测算法实现采煤机轨迹预测的解决方案,从而实现采煤机的智能控制。针对采煤机轨迹预测过程中数据样本较少的问题,提出了基于滑动窗口的灰色马尔可夫链预测模型。该模型具有算法相对简单、对随机事件响应能力强、预测精度高、计算速度快等特点。通过仿真实验,确定了滑动窗宽度,同时对预测算法进行了验证。仿真实验结果表明,该预测算法响应速度快,预测精度高,适合实际应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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