• DocumentCode
    3166174
  • Title

    A neural network expert system for evaluating the mining conditions of multiple mineral deposits of Gushan Mine

  • Author

    Zheng, Minggui ; Zhang, Ying ; Cai, Sijing ; Wang, Wenxiao

  • Author_Institution
    Min. Trade & Investment Res. Center, Jiangxi Univ. of Sci. & Technol., Ganzhou, China
  • fYear
    2011
  • fDate
    8-10 Aug. 2011
  • Firstpage
    38
  • Lastpage
    42
  • Abstract
    There are usually multiple mineral deposits in one mineral area, so comprehensive exploitation is very important. Based on the interpretation of the working mechanism of neural network expert system, first, a mining condition evaluation index system was designed; second, according to the mining conditions of the mineral deposit, a three-layer neural network was built; third, a detailed study on knowledge acquisition and expression of the neural network expert system was made; Finally, the neural network expert system was trained and analyzed, which show that it can be used for the identification of the mining conditions, the mining scale, the total investment and the internal rate of return, thereby can provide scientific decision-making references for the mines from rational mining sequence, scientific arrangement of investment funds to the rational mining scales.
  • Keywords
    expert systems; knowledge acquisition; mineral processing industry; mining industry; neural nets; Gushan Mine; investment funds; knowledge acquisition; knowledge expression; mining condition evaluation index system; multiple mineral deposits; neural network expert system; scientific decision-making references; Electromagnetic compatibility; Expert systems; Indexes; Investments; Iron; Minerals; investment; mining conditions; mining scale; multi mineral deposits; neural network expert system;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), 2011 2nd International Conference on
  • Conference_Location
    Deng Leng
  • Print_ISBN
    978-1-4577-0535-9
  • Type

    conf

  • DOI
    10.1109/AIMSEC.2011.6010205
  • Filename
    6010205