• DocumentCode
    3283289
  • Title

    Research on Data Fusion System of Fire Detection Based on Neural-network

  • Author

    Pei, Yanan ; Gan, Fangcheng

  • Author_Institution
    Sch. of Electr. & Electron. Eng., East China Jiaotong Univ., Nanchang, China
  • fYear
    2009
  • fDate
    16-17 May 2009
  • Firstpage
    665
  • Lastpage
    668
  • Abstract
    There are some serious problems in traditional fire detection methods and technology, such as lower grade intelligence, high mis-warming rate, delay-warming etc. In this paper, multi-information parameter in the early fire was regarded as early information of fire. This information was trained by neural network technology. Result would be gain with data fusion technology. And the result of data fusion could be confirmed as bases of fire and apply in the fire detection. A 3-layers data fusion structure is used in the neural network. In this fire detecting system, the fire experiential characteristic and the fire data-fitting characteristics of fire signal data are used by the fuzzy inference system to get the last fire probability. Finally simulation experiments are done for typical flaming fire, typical smoldering fire and the fire under typical disturbance signals´ in environment. And satisfactory results are obtained.
  • Keywords
    fires; fuzzy neural nets; fuzzy reasoning; sensor fusion; data fusion system; fire data-fitting characteristic; fire detection; fire experiential characteristic; fire signal data; flaming fire; fuzzy inference system; neural network; typical smoldering fire; Chemical processes; Chemical products; Circuits; Combustion; Data engineering; Delay; Fires; Neural networks; Production; Temperature; data fusion; fire detection; fuzzy reasoning; intelligent control; neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits, Communications and Systems, 2009. PACCS '09. Pacific-Asia Conference on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-0-7695-3614-9
  • Type

    conf

  • DOI
    10.1109/PACCS.2009.134
  • Filename
    5231970