摘要
水稻稻瘟病这种灰色系统具有复杂的非线性。综合利用蚁群灰色GM(1,1,θ)预测模型和RBF神经网络预测模型的特点,建立了蚁群灰色RBF神经网络组合预测模型。经过14年对水稻稻瘟病的预测分析,得出蚁群灰色神经网络模型的预测精度高达96.77%,验证了预测模型的有效性。
Rice blast, the grey system possesses complex non-lineafity. By combining the features of ant colony grey prediction model and RBF neural network prediction model, the combined prediction model of ant colony grey RBF neural network is established. Having been predicting and analyzing rich blast for 14 years, up to 96.77% prediction accuracy is obtained with ant colony neural network model, the effectiveness of the prediction model is verified.
出处
《自动化仪表》
CAS
北大核心
2013年第2期30-33,共4页
Process Automation Instrumentation
基金
黑龙江省教育厅科学技术研究基金资助项目(编号:12511356
12521378)
关键词
稻瘟病
灰色系统
蚁群优化算法
RBF神经网络
预测模型
Rice blast Grey system Ant colony optimization algrithm RBF neural network Prediction model
