• DocumentCode
    478011
  • Title

    Analysis on Weak Electric Signals of Plants by the Autoregressive Model

  • Author

    Wang, Lanzhou ; Wang, Miao ; Zhao, Jiayin

  • Author_Institution
    Coll. of Metro logical Technol. & Eng., China Jiliang Univ., Hangzhou
  • Volume
    1
  • fYear
    2008
  • fDate
    18-20 Oct. 2008
  • Firstpage
    171
  • Lastpage
    175
  • Abstract
    Models of the autoregressive model of weak electric signals in two plants were set up for the first time: Crassula portulacea wais x (n) = 1.95516 x (n-1) - 0.91114 x (n-2) -0.41287 x (n-4) + 0.65251 x (n-5) - 0.12294 x (n-6) - 0.28019 x (n-7) - 0.19178 x (n-8) + 0.54703 x (n-9) - 0.12636 x (n-10) - 0.15383 x (n-11) + 0.21684 x (n-14) - 0.3721 x (n-15) + 0.37012 x (n-17) - 0.2541 x (n-19) + 0.15452 x (n-21) - 0.10477 x (n-23) + 0.03347 x (n-25). And Aloe vera var. chinensis was x (n) = 1.9591 x (n-1) - 1.4927 x (n-2) + .07205 x (n-3) - 0.80219 x (n-4) + 0.44582 x (n-5) - 0.30807 x (n-6) + 0.19658 x (n-7) - 0.34506 x (n-8) + 0.52419 x (n-9) - 0.339 x (n-10) + 0.16681 x (n-11) - 0.09664 x (n-12) + 0.03625 x (n-17) - 0.02005 x (n-18). The results of the AR model to forecast 10 values of the weak electric signals are well, and can be used as the preferences for the intelligent autocontrol system based on the adaptive characteristic of plants to achieve the energy saving on agricultural productions.
  • Keywords
    autoregressive processes; forecasting theory; Crassula portulacea; adaptive characteristics; agricultural productions; autoregressive model; energy saving; forecasting theory; intelligent autocontrol system; weak electric signals; Automatic testing; Biological system modeling; Brain modeling; Educational institutions; Mathematical model; Plants (biology); Reactive power; Signal analysis; Signal processing; System testing; AR model; model fitting; plant; weak electric signal;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation, 2008. ICNC '08. Fourth International Conference on
  • Conference_Location
    Jinan
  • Print_ISBN
    978-0-7695-3304-9
  • Type

    conf

  • DOI
    10.1109/ICNC.2008.96
  • Filename
    4666833