The Research of Prediction of Pests Based on Fuzzy RBF Neural Network
Yanling Wei, Lin Fei-Ying · 2009
The prediction for pests is usually amphibious, relevant, complicated, and nonlinear. The neural network has the problem of decreasing generalization ability in the prediction of small samples. This paper presents a method of the prediction of pests based on fuzzy RBF neural network. A learning algorithm of adjusting the center, width and weight of the RBF is put forward. The use of fuzzy clustering technique for data preprocessing and the use of RBF neural network for nonlinear prediction have solved the problem of the ambiguity, relevance and non-linear of prediction of pests. The simulation results show that the outcomes of the predictions of pests based on fuzzy RBF neural network are accurate. This method is simple and practical. Especially in the condition of small samples or larger relevance among samples, the use of the method can achieve better results.