基于小波分析与BP神经网络的人体血压预测

及时、准确预测人体血压变化从而预防人体血压不稳定导致的病情加重的情况发生显得越来越重要.对此本文提出一种基于小波分析与BP神经网络组合的人体血压预测模型，该模型利用小波分解重构法对非平稳的人体血压序列进行分解重构计算，分离出原始序列中的高频细节分量和低频趋势分量，再利用BP神经网络预测算法对各层分量建立预测模型，最后将两种模型的预测值进行叠加，得到原始血压序列的预测值.研究表明，该组合预测模型的预测精度明显高于传统BP神经网络预测模型的预测精度，为人体血压预测提供了一种有效可靠的组合预测方法.

Prediction of Human Blood Pressure Based on Wavelet Analysis and BP Neural Network

It is becoming increasingly important to make timely and accurate prediction of human blood pressure changes in order to prevent the exacerbation caused by the instable human blood pressure .This paper proposes a prediction model of human blood pressure based on the combination of wavelet analysis and BP neural networks. This model uses the wavelet decomposition and reconstruction method to decompose and reconstruct non-stationary human blood pressure sequence, separating the high frequency components and the low frequency components in the original sequence, then the BP neural network prediction algorithm is used to establish the prediction model for each layer. Finally, the predicted values of the two models are added to obtain the predicted values of the original series. The results show that the prediction accuracy of the combined forecasting model is obviously higher than that of the traditional BP neural network prediction model, which provides an effective and reliable combination forecasting method for human blood pressure prediction.