基于人工蜂群优化高斯过程的运动想象脑电信号分类

发布时间：2018-06-13 04:18

  本文选题：脑电信号 + 高斯过程分类　； 参考：《传感技术学报》2017年03期

【摘要】：针对传统的高斯过程采用共轭梯度法确定超参数时对初值有较强依赖性且易陷入局部最优的问题,提出了一种基于人工蜂群优化的高斯过程分类方法,用于脑电信号的模式识别。首先,构建高斯过程模型,选择合适的核函数且确定待优化的参数。然后,选取识别错误率的倒数为适应度函数,使用人工蜂群算法搜索寻找出限定范围内可以取得最优准确率的超参数。最后,采用参数优化后的高斯过程分类器对样本分类。分别采用2008年竞赛数据集BCI CompetitionⅣData Set 1和2005年数据集BCI CompetitionⅢData SetⅣa对所提方法进行验证,并与支持向量机(SVM)、人工蜂群优化的支持向量机(ABC-SVM)、高斯过程分类(GPC)方法进行比较,实验结果表明了所提方法的有效性。
[Abstract]:In order to solve the problem that the traditional Gao Si process has strong dependence on the initial value and is easy to fall into local optimum when using conjugate gradient method to determine the superparameters, a Gao Si process classification method based on artificial bee colony optimization is proposed. Pattern recognition for EEG signals. Firstly, the Gao Si process model is constructed, the appropriate kernel function is selected and the parameters to be optimized are determined. Then, the inverse of the recognition error rate is selected as the fitness function, and the artificial bee colony algorithm is used to search for the super-parameters which can obtain the best accuracy in the limited range. Finally, the Gao Si process classifier with optimized parameters is used to classify the samples. The proposed method is validated by BCI Competition 鈪，

本文编号：2012688