Application of artificial neural network model to safety assessment for coal mine

被引:0
|
作者
Zeng Qi [1 ]
Xu Jun [1 ]
Wang Suling [1 ]
机构
[1] Henan Polytech Univ, Sch Econ & Management, Jiaozuo 454000, Henan, Peoples R China
来源
PROGRESS IN SAFETY SCIENCE AND TECHNOLOGY, VOL 6, PTS A AND B | 2006年 / 6卷
关键词
coal mine; safety assessment; neural network;
D O I
暂无
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Based on artificial neural network theory, the complexity of safety assessment system is analyzed and the assessment index of coal mine safety is confirmed. A neural network model with multi-hierarchic structure was built based on error back-propagation (BP) algorithm. The conjunction weights of the neural network are continuously modified layer by layer from output layer to input layer in the process of neural network training to reduce the errors between the anticipated and actual outputs. A case study was conducted and it verified the validity and accuracy of the proposed method. The method used in coal mine safety production assessment preventing from in the past with level analytic approach and fuzzy to judge the deficiency that law brought synthetically, make coal mine more perfect in safety assessment.
引用
收藏
页码:47 / 50
页数:4
相关论文
共 50 条
  • [21] Set Pair Analysis Model and Application for Safety Evaluation on Mining Condition in Coal Mine
    Li Fanxiu
    Mei Ping
    PROGRESS IN SAFETY SCIENCE AND TECHNOLOGY, VOL. VIII, PTS A AND B, 2010, 8 : 207 - 211
  • [22] Application of artificial neural network for fuzzy logic based leanness assessment
    Vimal, K. E. K.
    Vinodh, Sekar
    JOURNAL OF MANUFACTURING TECHNOLOGY MANAGEMENT, 2013, 24 (02) : 274 - 292
  • [23] Risk assessment of mine safety equipment - Gas measurement system in a coal mine
    Tanaka, A
    Komai, T
    Noda, K
    Nakagawa, Y
    Sagisaka, M
    Kishimoto, A
    Jinguji, M
    Kosugi, M
    Kunimatsu, S
    Isei, T
    PSAM 5: PROBABILISTIC SAFETY ASSESSMENT AND MANAGEMENT, VOLS 1-4, 2000, (34): : 2025 - 2030
  • [24] Assessment of artificial neural network to identify compositional differences in ultrahigh-resolution mass spectra acquired from coal mine affected soils
    Solihat, Nissa Nurfajrin
    Son, Seungwoo
    Williams, Elizabeth K.
    Ricker, Matthew C.
    Plante, Alain F.
    Kim, Sunghwan
    TALANTA, 2022, 248
  • [25] An Optimized Multisource Bilinear Convolutional Neural Network Model for Flame Image Identification of Coal Mine
    Zhang, Li
    Zhu, Yuqin
    Wu, Hao
    Li, Kun
    IEEE ACCESS, 2022, 10 : 47284 - 47300
  • [26] Theory and Application of Coal Mine Safety Foundation Innovative Management
    Gao, Rui
    PROCEEDINGS OF THE 2012 INTERNATIONAL CONFERENCE ON MANAGEMENT INNOVATION AND PUBLIC POLICY (ICMIPP 2012), VOLS 1-6, 2012, : 3456 - 3459
  • [27] Application of system methods in safety management of coal mine in China
    Jing, GX
    Zheng, Y
    PROGRESS IN SAFETY SCIENCE AND TECHNOLOGY, VOL V, PTS A AND B, 2005, 5 : 1837 - 1840
  • [28] Discussion on Application of IOT Technology in Coal Mine Safety Supervision
    Zhang Yinghua
    Fu Guanghua
    Zhao Zhigang
    Huang Zhian
    Li Hongchen
    Yang Jixing
    INTERNATIONAL SYMPOSIUM ON SAFETY SCIENCE AND ENGINEERING IN CHINA, 2012, 2012, 43 : 233 - 237
  • [29] Management and Assessment of Coal Mine Safety Based on Man-Machine-Environment System
    Deng Qigen
    Wei Jianping
    Cao Qinggui
    PROGRESS IN SAFETY SCIENCE AND TECHNOLOGY, VOL VII, PTS A AND B, 2008, 7 : 98 - 101
  • [30] A Kind of Coal Mine Safety Control Model Based on Cybernetics
    Qi, Lixia
    Yang, Xue
    INNOVATIVE COMPUTING AND INFORMATION, ICCIC 2011, PT I, 2011, 231 : 124 - 131