摘要
目的针对采煤机记忆截割不准确、自动化程度不高的问题,方法本文提出一种基于KGRU神经网络的采煤机记忆截割算法,此算法具有更适合处理长时序数据的特点,将算法与采煤机记忆截割结合起来,可以减少采煤过程中滚筒的损坏同时保护工人生命安全。该算法在深层门控循环单元(GRU)的输入端引入比例因子K,用比例因子K表现不同时刻数据的重要程度,以加强模型对长时序数据的记忆性,进而提高记忆截割精度。在模型训练阶段利用随机搜索算法(RS)对深层K-GRU神经网络的超参数选择进行优化,加快模型训练速度。结果实验中使用Python完成K-GRU模型构建与超参数优化,使用随机搜索算法可以在更短时间内得到超参数最优解,得到超参数epochs为317、batch_size为70的最优解共花费154 s,在最优解情况下计算模型对真实采煤数据预测的误差,得到K-GRU的loss值为0.0467、R2为0.9578、EVS为0.9656、ME为0.0833。结论最终表明,优化后的深层K-GRU模型在解释方差得分、最大误差和可决系数方面均优于SVM、KNN、LSTM、RNN和普通GRU模型,显著提高了采煤机记忆截割的适用性和准确性。
Objective Aiming at the inaccurate memory cutting and the low degree of automation of shearer,Methods This paper proposed a shearer memory cutting algorithm based on K-GRU neural network.This algorithm was more suitable for processing long-time sequence data.Combining the algorithm with the memory cutting of shearer can reduce the damage of the drum during the coal mining process and protect the safety of workers’lives.The algorithm introduced the proportional factor K at the input end of the deep gated recurrent unit(GRU),and used the proportional factor K to show the importance of data at different times and to strengthen the memory of the model for long-time sequence data,thereby improving the accuracy of memory cutting.In the model training stage,the random search algorithm(RS)was used to optimize the hyperparameter selection of the deep K-GRU neural network to speed up the training speed of the model.Results In the experiment,Python was used to complete the construction of the K-GRU model and the optimization of hyperparameters.Using the random search algorithm,the optimal solution of the hyperparameter could be obtained in a shorter time.The optimal solution of the hyperparameter epochs of 317 and the batch_size of 70 costed a total of 154 s.In the case of the optimal solution,the error of the calculation model’s prediction of the real coal mining data was 0.0467,R2 was 0.9578,EVS was 0.9656,and the ME was 0.0833.Con⁃clusion Finally,it showed that the optimized deep K-GRU model was better than SVM,KNN,LSTM,RNN and ordinary GRU models in terms of interpretation of variance score,maximum error and decision coefficient,which significantly improved the applicability and accuracy of shearer memory cutting.
作者
安葳鹏
闫鹏皓
张文博
孙旭旭
AN Weipeng;YAN Penghao;ZHANG Wenbo;SUN Xuxu(School of Computer Science and Technology,Henan Polytechnic University,Jiaozuo 454000,Henan,China)
出处
《河南理工大学学报（自然科学版）》
CAS
北大核心
2024年第1期96-104,共9页
Journal of Henan Polytechnic University（Natural Science）
基金
国家自然科学基金资助项目(61872126)
河南省高校重点研究基金资助项目(20A520015)。
关键词
门控循环单元
记忆截割
随机搜索算法
强化因子
采煤机
gate recurrent unit
memory cutting
random search algorithm
strengthening factor
shearer
