基于 LDA 与 QDA 的自增强分类器在肌电模式识别中的应用

摘要

In order to adapt to the time‐varying characteristics of EMG signals in pattern classification , a novel adaptive self‐enhancing classification method is proposed .Based on the traditional static classi‐fiers (LDA and QDA ) ,this proposed method introduces a new computing method for updating the pa‐rameters of the covariance matrix and the mean vector ,w hich can be used to update the parameters during testing stage .Experimental results reveal that the proposed method can significantly reduce the computational complexity and improve the classification performance .%为使分类器适应肌电模式识别中肌电信号的时变性，提出了一种新的具有自适应能力的自增强分类方法，该方法在传统的静态分类器（LDA分类器和QDA分类器）的基础上，引入了一个新的参数更新算法，通过更新方差矩阵和均值向量实现参数求解，使其能在测试阶段对分类器参数进行动态更新。实验结果表明，该方法不仅降低了计算复杂度，而且显著提高了分类器的识别性能。