基于相空间重构技术和GA-BPNN算法的小麦条锈病受灾率预报模型

Research on chaotic characteristics of the disaster rate of crops and its GA-BPNN forecasting model

利用混沌理论,对湖北省某地区小麦条锈病受灾率进行混沌特征验证,然后利用BP神经网络非线性逼近器能力,建立预测模型,利用重构相空间,确定神经网络的输入节点数及输入值,并引入遗传算法优化BP神经网络参数,对受灾率进行了成功预报。

The chaos theory is used to test chaotic characteristics of the disaster rate of wheat rust certain part of Hubei province. Then the forecasting model is established to forecast the disaster rate by combining BP-NN with GA. With reconstruction of phase space,determining the input numbers and values and the optimized BP algorithms,the disaster rate has been successfully forecasted.