【摘要】 针对地震紧急救援阶段血液需求特点,提出用于预测其需求量的,基于小波分析(WA)和支持向量机(SVM)的组合方法(WA-SVM)。首先对原始血液需求进行小波分析,然后确定SVM输入向量和输出向量集合,构建各层序列的SVM预测模型,对血液需求进行预测。汶川地震案例表明,该方法的预测精度优于经验模态分解和SVM的组合预测模型以及SVM单项预测模型。更多还原

【Abstract】 A combined method of WA and SVM was worked out for forecasting the blood demand in emergency rescue after earthquake.The sample demand series was first mapped into several different frequency scales using wavelet transformation,and then a SVM is established for each scale.The WA-SVM forecasting method was then used to forecast blood demand in emergency rescue taking a case in Wenchuan earthquake as an example.The forecasting results indicate that the WA-SVM forecasting method has better performance both in accuracy and applicability in comparison with the empirical mode decomposition(EMD)-SVM combined method,and SVM method.更多还原