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机构地区:[1]晋中学院计算机科学与技术学院,山西晋中030600 [2]山西大学计算机与信息技术学院,太原030006 [3]山西大学计算智能与中文信息处理教育部重点实验室,太原030006
出 处:《计算机工程与应用》2011年第10期244-248,共5页Computer Engineering and Applications
基 金:科技部科技型中小企业技术创新基金(No.07c26211401103);山西省科技攻关项目(No.051124-5);山西省教育厅科技开发项目(No.20041201);太原市科技明星专项项目(No.08121005);太原市科技局创新计划项目(2006);太原市科技局中小企业创新项目(No.0503037)
摘 要:近年来各种矿难频发,特别是瓦斯事故时见于报端。瓦斯事故通常伴随着较高的瓦斯浓度,因此,预测未来时刻的瓦斯浓度是预测瓦斯事故的有效手段,对煤矿的安全生产具有十分重要的意义。对混沌理论中的C-C方法进行简化,并用这种方法对5大煤矿的瓦斯浓度监测数据构成的时间序列进行相空间重构,依据数据的实际情况确定其最佳时延和嵌入维,然后用加权一阶局域法对下一时刻的瓦斯浓度进行预测。实验结果表明瓦斯浓度时间序列具有明显的混沌特性,且当时间序列长度为500时,计算量适中且预测结果较优,对500个异常瓦斯浓度预测的均方误差达到0.122024,从而可用于瓦斯事故的预测,为煤矿及时采取通风等措施提供决策依据。In recent years,mine disasters are occuring frequently,especially gas accidents which are often reported in newspapers.Gas accidents are usually accompanied by a high concentration of the gas,so gas concentration predicting of the future moment is an effective means of gas accident forecasting and has very important significance to production safety in coal mines.This paper simplifies C-C method on chaos theory,and reconstructs phase space using time series of gas concentration values by monitoring from five coal mines.The paper chooses an appropriate value for the delay time and the embedding dimension according to the real values of gas concentration,and then forecasts the gas concentration of the next moment using weight one-rank local-region method.The experimental results show time series of gas concentration have obviously the chaotic property.The results also show moderate calculation and better forecast results when the length of time series is 500. The mean square error of forecast results of 100 abnormal gas concentration values is 0.122 024,thus the method can be used for the prediction of gas accidents and provide basis for decision making in coal mines,for example,taking measures such as ventilationin in a timely manner.
分 类 号:TD712[矿业工程—矿井通风与安全]
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