基于EMD和LVQ的信号特征提取及分类方法

针对非平稳、非线性、微弱信号难以分析和处理的特点,本文提出了一种基于经验模式分解和学习向量量化神经网络的信号处理和分类方法,并在生物信号处理领域(左、右手运动想象的脑电信号)进行了研究和应用.首先通过经验模式分解算法对脑电信号分解,然后选取主要固有模态函数分量并计算其绝对均值作为特征值,最后使用学习向量量化网络进行分类,并分别与支持向量机和误差反向传播神经网络分类算法进行了对比研究.实验结果表明,所提出的算法分类正确率达到了87％,相比于其余两种对比算法在特定的信号处理领域优越,具有一定的参考和研究价值.

Signal Feature Extraction and Classification Method Based on EMD and LVQ Neural Network

Non stationary, non-linear, and weak signals are difficult to analyze and process. A novel signal processing method based on empirical mode decomposition (EMD) and learning vector quantization (LVQ) neural network is proposed and applied in the field of biological signal processing (left and right hands move imagery electroencephalogram (EEG) signal). Firstly, EMD is used to decompose EEG signal. Secondly, the major intrinsic mode function components are extracted and their mean absolute values are calculated as the features. Finally, LVQ is used to finish the classification task. Then the results are compared with the support vector machine and error back propagation neural network classification algorithms. The experimental results show that the classification accuracy rate of the proposed algorithm reaches 87%. Compared to the other two contrast algorithms, the new algorithm has better performance in the specific signal processing field and thus has high reference and research value.