基于极限学习机的中医脉象识别方法

针对中医脉象模糊性强、种类繁多、特征复杂的特点,以及传统模糊聚类方法、BP神经网络识别方法的不足,提出了一种基于极限学习机(extreme learning machine,ELM)的脉象识别方法.该方法通过提取脉象信号的特征向量,然后利用ELM对特征向量进行了训练和分类.实验结果表明,本文所提出的脉象识别方法与传统模糊聚类方法、BP神经网络方法和支持向量机方法相比,识别正确率分别提高21%,9%和5%.这表明所提出的方法对脉象的分类判别能取得良好的效果.

Recognition Method of Traditional Chinese Medicine Pulse Conditions Based on Extreme Learning Machine

In the light of the ambiguity， variety， and complexity of traditional Chinese medicine(TCM) pulse conditions， and the shortcomings of traditional fuzzy cluster methods and backpropagation(BP) neural network methods， a novel method using the extreme learning machine(ELM) was proposed to detect the pulse conditions. This method identifies pulse condition by using the ELM to train and classify the characteristic vectors obtained by the pulse condition. The experimental results show that comparing with the traditional fuzzy cluster methods， BP neural network method and support vector machine method， the accuracy of proposed method is respectively increased by 21 percent， 9 percent and 5 percent， which shows that this is a better pulse condition estimation using proposed method.