机译:应用粒子群优化,支持向量机和人工神经网络的小流域水土流失特征分析
College of Water Conservancy and Civil Engineering, China Agricultural University, 100083 Beijing, People's Republic of China State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 100085 Beijing, People's Republic of China;
rnResearch Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, 100080 Beijing, People's Republic of China;
rnState Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 100085 Beijing, People's Republic of China;
rnCollege of Water Conservancy and Civil Engineering, China Agricultural University, 100083 Beijing, People's Republic of China;
rnInternational College at Beijing, China Agricultural University, 100083 Beijing, People's Republic of China;
rnCollege of Water Conservancy and Civil Engineering, China Agricultural University, 100083 Beijing, People's Republic of China;
rnInstitute of Water Conservancy Science of Inner Mongolia, 010020 Hohhot, People's Republic of China;
soil erosion; particle swarm algorithm; support vector machine; small watershed; characteristics extraction;
机译:利用粒子群算法,支持向量机和人工神经网络对小流域水土流失特征进行分析
机译:基于粒子群优化和人工蜂群优化的支持向量机的癌症分类
机译:基于支持向量机的癌症分类优化粒子群优化和人工蜜蜂殖民地优化
机译:基于粒子群优化(PSO)的混合方法,人工蜂菌落(ABC)特征选择和支持向量机的基因选择
机译:使用粒子群优化对训练前的人工神经网络实现一致的近似最佳模式识别精度。
机译:基于粒子群优化和人工蜂群优化的支持向量机的癌症分类
机译:利用粒子群算法,支持向量机和人工神经网络对小流域水土流失特征进行分析