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
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;
Conference_Titel :
Natural Computation, 2008. ICNC '08. Fourth International Conference on
Conference_Location :
Jinan
Print_ISBN :
978-0-7695-3304-9
DOI :
10.1109/ICNC.2008.96