基于小波神经网络和BP神经网络的麦蚜发生期预测对比
Comparison of occurrence periods of wheat aphids based on artificial neural network and wavelet neural network prediction systems

为建立更准确、稳定的病虫害预测预报模型,减少农作物病虫害损失、提高农作物产量与质量,运用主成分分析法从42个基础气象因子中整合形成8个新的自变量输入模型,采用试凑法对网络关键参数进行筛选,用2002-2011年数据进行网络训练,建立了以Morlet小波函数为传递函数的小波神经网络模型,并与以Sigmoid函数为传递函数的BP神经网络模型进行了比较。在小波神经网络训练过程中,有6年拟合精度在90%以上,平均拟合精度为89%,预测结果MAPE值为4.1939,MSE值为5.9764;在BP神经网络的训练过程中,有4年拟合精度超过90%,平均拟合精度仅为81.07%,预测结果中MAPE值为6.4694,MSE值为8.2457。从训练结果看,小波神经网络更能准确描述麦蚜发生期的变化规律,其拟合能力较BP神经网络好;从预测精度和模型的稳定性来看,小波神经网络好于BP神经网络。
To build a more accurate and stable prediction model for insect pests, take precautions against insect pests, reduce the crop loss and increase crop yields and quality, eight new independent variables were developed from 42 basic meteorological factors by using the method of principal component analysis and prediction models in this study. Key parameters were selected with cut-and-trial method. Wavelet Neural Network (WNN) model was established with Morlet wavelet function as the transfer function and compared with Back Propagation Neural Network (BPNN) model with Sigmoid function as the transfer function. In WNN, the training data from six out of ten years showed a fitting precision of more than 90% with an average fitting precision of 89%. The predicted mean absolute percentage error (MAPE) and mean square error (MSE) value were 4.1939 and 5.9764, respectively. In BPNN, the fitting precision was above 90% for four of ten years with an average precision rate of 81.07%. The predicted MAPE and MSE values were 6.4694 and 8.2457, respectively. The comparison results of different models showed that WNN more accurately described the developed pattern of wheat aphids in the field and displayed better fitting ability than BPNN. Besides, WNN possessed stronger prediction accuracy and stability than BPNN.