A forecast of RBF neural networks on electrical signals in Senecio cruentus

Abstract

Weak electrical signals in Senecio cruentus 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 model was set up to forecast the weak signals of all plants in the globe. Testing result shows that it is feasible to forecast the plant electrical signal for a short period. The forecast data is significant and can be used as preferences for the intelligent automatic control system based on the electrical signal adaptive characteristics of plants to achieve the energy saving on the production both greenhouses and or plastic lookum. © 2010 Springer-Verlag.