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Proceedings Paper

RBF neural network prediction on weak electrical signals in Aloe vera var. chinensis
Author(s): Lanzhou Wang; Jiayin Zhao; Miao Wang
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Paper Abstract

A Gaussian radial base function (RBF) neural network forecast on signals in the Aloe vera var. chinensis by the wavelet soft-threshold denoised as the time series and using the delayed input window chosen at 50, is set up to forecast backward. There was the maximum amplitude at 310.45μV, minimum -75.15μV, average value -2.69μV; and <1.5Hz at frequency in Aloe vera var. chinensis respectively. The electrical signal in Aloe vera var. chinensis is a sort of weak, unstable and low frequency signals. A result showed that it is feasible to forecast plant electrical signals for the timing by the RBF. The forecast data can be used as the preferences for the intelligent autocontrol system based on the adaptive characteristic of plants to achieve the energy saving on the agricultural production in the plastic lookum or greenhouse.

Paper Details

Date Published: 13 October 2008
PDF: 7 pages
Proc. SPIE 7127, Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence, 71271G (13 October 2008); doi: 10.1117/12.806563
Show Author Affiliations
Lanzhou Wang, China Jiliang Univ. (China)
Jiayin Zhao, Wuxi Entry-Exit Inspection and Quarantine Bureau (China)
Miao Wang, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 7127:
Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence

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