NDLI: Neural networks prediction of electrical signals at xylem in Osmanthus fragrans

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2010 International Conference on Computer Application and System Modeling (ICCASM 2010)

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Neural networks prediction of electrical signals at xylem in Osmanthus fragrans

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Volume 1 - 2010

Neural networks prediction of electrical signals at xylem in Osmanthus fragrans

Content Provider	IEEE Xplore Digital Library
Author	Jinli Ding Lanzhou Wang
Copyright Year	2010
Description	Author affiliation: College of Life Sciences, China Jiliang University, Hangzhou, 310018 Zhejiang, China (Lanzhou Wang) \|\| College of Metrological Technology and Engineering, China Jiliang University, Hangzhou, 310018 Zhejiang, China (Jinli Ding)
Abstract	Plant weak electrical signals in the xylem of Osmanthus fragrans were tested by a touching test system of self-made double shields with platinum sensors. Tested data of electrical signals denoised by the wavelet soft threshold and using Gaussian radial base function (RBF) as the time series at a delayed input window chosen at 50. An intelligent RBF forecasting system was constructed to forecast the signals in the xylem. Through the study of 1450 stylebooks that used the electrical signal after de-noised in the xylem, the effect on the inner examination of the obtained RBF neural network was very well in coincidence with and can be used for a forecast of the plant electrical signal at the time domain in the timing. Result shows that it is feasible to forecast the plant growth for a short period. The forecast data can be used as an important preference for the intelligent automatic control system based on the adaptive characteristic of xylem in plants to achieve the energy saving on agricultural production both the greenhouse and /or the plastic lookum.
File Size	574084
File Format	PDF
ISBN	9781424472352
e-ISBN	9781424472376
DOI	10.1109/ICCASM.2010.5620494
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Osmanthus fragrans Frequency modulation RBF neural network Wavelet soft threshold denoising Weak electrical signal Artificial neural networks Intelligent control
Content Type	Text
Resource Type	Article

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