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.
Radial base function (RBF) neural network wavelet soft threshold denoising plant weak electrical signal intelligent control Dahlia pinnata
Lanzhou Wang Jinli Ding
College of Life Sciences, China Jiliang University, Hangzhou 310018, Zhejiang, China College of Metrological Technology and Engineering, China Jiliang University, Hangzhou 310018, Zheji
国际会议
2010 International Conference on Image Analysis and Signal Processing(2010 图像分析与信号处理国际会议 IASP 10)
厦门
英文
408-412
2010-04-12（万方平台首次上网日期，不代表论文的发表时间）