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Forecast and Processing of Weak Electrical Signals in Clivia miniata by RBF Neural Networks

Authors :
Ding, Jin Li
Wang, Lan Zhou
Source :
Advanced Materials Research; March 2011, Vol. 216 Issue: 1 p388-392, 5p
Publication Year :
2011

Abstract

Original weak electrical signals in Clivia miniata 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 set up to forecast the signal in plants. Testing result shows that it is feasible to forecast the plant electrical signal for a short period. The forecast data can be used as an important preferences for the intelligent automatic control system based on the adaptive characteristic of plants to achieve the energy saving on agricultural production both the greenhouse and /or the plastic lookum.

Details

Language :
English
ISSN :
10226680
Volume :
216
Issue :
1
Database :
Supplemental Index
Journal :
Advanced Materials Research
Publication Type :
Periodical
Accession number :
ejs23550539
Full Text :
https://doi.org/10.4028/www.scientific.net/AMR.216.388