Title :
Method of Plant Growth Modeling Based on Genetic Algorithm and RBF Network
Author :
Zhenjiang, Cai ; Yihua, Hu ; Yumei, Sun ; Shunbin, Hu
Author_Institution :
Agric. Univ. of Hebei, Baoding
Abstract :
The plant growth model is very difficult to be set up. Because the relations between the growth parameters and surround envelopment parameters are very complex. A new method that using artificial neural network for plant growth modeling is presented. For improving the algorithm convergence rate, the radial basis function (RBF) network is adopted. As an example of this method, the prediction of tomato stem daily growth and its surround relation is also presented. The experiment results show that the method is effective for plant growth modeling.
Keywords :
botany; genetic algorithms; radial basis function networks; RBF network; artificial neural network; genetic algorithm; growth parameters; plant growth modeling; radial basis function network; surround envelopment parameters; Artificial neural networks; Convergence; Feedforward systems; Genetic algorithms; Instruments; Mathematical model; Mechanical variables measurement; Neural networks; Plants (biology); Radial basis function networks; Plant modeling; artificial neural network; genetic algorithm; radial basis function;
Conference_Titel :
Electronic Measurement and Instruments, 2007. ICEMI '07. 8th International Conference on
Conference_Location :
Xi´an
Print_ISBN :
978-1-4244-1136-8
Electronic_ISBN :
978-1-4244-1136-8
DOI :
10.1109/ICEMI.2007.4350771
