RBF neural network prediction on weak electrical signals in Aloe vera var. chinensis

Lanzhou Wang; Jiayin Zhao; Miao Wang

doi:10.1117/12.806563

13 October 2008 RBF neural network prediction on weak electrical signals in Aloe vera var. chinensis

Lanzhou Wang, Jiayin Zhao, Miao Wang

Proceedings Volume 7127, Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence; 71271G (2008) https://doi.org/10.1117/12.806563
Event: Seventh International Symposium on Instrumentation and Control Technology, 2008, Beijing, China

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.

Citation Download Citation

Lanzhou Wang, Jiayin Zhao, and Miao Wang "RBF neural network prediction on weak electrical signals in Aloe vera var. chinensis", Proc. SPIE 7127, Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence, 71271G (13 October 2008); https://doi.org/10.1117/12.806563

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