Information Forecast of Electrical Signals in Dahlia Pinnata by Neural Networks

Electrical signals in Dahlia pinnata were tested by a touching test system of self-made double shields with platinum sensors and tested data of electrical signals denoised by the wavelet soft threshold and also using Gaussian radial base function (RBF) as the time series at a delayed input window chosen at 50. An intelligent RBF forecasting model was set up to forecast the information amalgamation of signals in plants. Result shows that it is feasible to forecast variation of plant electrical signals for a short period. Forecast data can be used as the preferences for the intelligent automatic control system based on the adaptive characteristic of plants both the greenhouse and /or plastic locum to achieve energy savings in agricultural and horticultural production.