混沌理论与BP网络融合的稻瘟病预测模型

为了能更有效地预测稻瘟病的发生，将混沌理论（G-P算法）与BP人工神经网络融合建立了稻瘟病预测模型，并运用QPSO算法优化BP神经网络，避免了BP算法易陷入局部极小值的缺陷。运用G-P算法对云南省凤庆县历年稻瘟病发病情况的历史数据进行了研究。研究发现最小嵌入空间维及K熵都为正数，故稻瘟病的发生具有一定的混沌特性，从而确定了模型输入层的个数。应用该模型对2001－2009年稻瘟病发生程度进行预测，并与其他预测模型进行比较。结果表明：该模型预测的准确率和收敛速度明显高于其他预测模型，且预测结果有效可行，为解决

Forecasting model on rice blast based on BP neural network and chaos theory

To foresee rice blast efficiently, a new forecasting model was established by integrating the chaos theory into the BP artificial neural network (ANN). The BP neural network was optimized by the QPSO algorithm. In this new model, the shortcoming that the BP network algorithm is easy to fall into local minimum value has been avoided. Based on the data of rice blast occurrences over the years in Fengqing County, Yunnan, China, the G-P algorithm was applied to study their K entropy and inserting space dimension. It was found that their values were all positive. So the rice blast occurrence has some chaos characteristics, and the number of model input layer can be determined. This model was used to make forecasts of rice blast in this county during 2001-2009, and was compared with other forecasting models. The results proved that the accuracy rate and convergence velocity of this model were higher than other forecasting models, and its forecasting results were also effective and believable. The new model can provide a new way to solve the problems, such as pattern recognition, classification, and forecasting of rice blast.