Forecast of RBF Neural Networks to Weak Electrical Signals in Plant

The original electrical signals in Crassula portulacea were tested by a touching test used platinum sensors in a system of self-made double shields. Tested data of the electrical signals were 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. The 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.