基于小波包分析和BP神经网络的中医脉象识别方法

利用小波变换具有揭示信号时频两域细节和局部特征的能力，提出了将脉象信号的小波包分析和BP神经网络相结合以达到识别中医脉象的新方法。首先对脉象信号作三层小波包分解，利用小波包分解系数重构信号。然后计算第三层从低频至高频八个频带的信号能量，以此能量构造出脉象信号的特征向量送入改进的BP神经网络进行训练。大量样本的实验证实该方法具有识别正确率高、速度快的优点。

Recognition Method of TCM Pulse Conditions Based on Wavelet Packet Analysis and BP Neural Networks

Using the abalities of revealing the signal details and the local characteristics in the time-frequency domains, this paper presents a pulse-condition recognition method that is based on wavelet packets analysis and BP neural networks. The pulse-condition signals are decomposed into three layers wavelet coefficients by which the pulse-condition signals are reconstructed. On the third layer wavelet signals, the energy values of eight frequency bands from low frequency to high frequency are calculated. The energy values are used as the characteristic vectors of the pulse-condition signals, which are sent to improved BP neural networks as charateristic vectors to be trained. The experiment results of 480 pulse-conditions show that the recognition rate of our method is rather high.