面向癫痫EEG自适应识别的迁移径向基神经网络

发布时间：2018-02-28 18:08

  本文关键词： 脑电图(EEG) 径向基神经网络 直推式迁移学习　出处：《计算机科学与探索》2016年12期 　论文类型：期刊论文

【摘要】：在癫痫脑电图(electroencephalogram,EEG)信号识别中,传统的智能建模方法要求训练数据集和测试数据集均服从相同的分布。但在实际应用中,某些情况并不能满足此条件,进而导致传统方法性能急剧下降。针对上述情况,引入迁移学习策略,提出了适用于数据分布迁移环境的直推式径向基神经网络(transductive radial basis function neural network,TRBFNN)。该方法在癫痫EEG信号识别中的实验结果表明:直推式径向基神经网络具有较好的场景迁移适应性,对训练数据和测试数据存在差异时,识别性能不会出现急剧恶化的现象。
[Abstract]:In the recognition of EEG electroencephalogramma (EGG) signals, the traditional intelligent modeling method requires that both the training data set and the test data set be distributed in the same way. However, in some practical applications, this condition cannot be satisfied. Therefore, the performance of the traditional methods drops sharply. In view of the above situation, the transfer learning strategy is introduced. A direct radial basis function neural network (TRBFNN) for data distribution migration environment is proposed. The experimental results of this method in EEG signal recognition show that the direct push radial basis function neural network has a good adaptability to scene migration. When there are differences between training data and test data, the recognition performance will not deteriorate sharply.
【作者单位】： 江南大学数字媒体技术学院;
【基金】：国家自然科学基金面上项目No.61170122 江苏省杰出青年基金项目No.BK20140001 新世纪优秀人才支持计划项目No.NCET-120882~~
【分类号】：R742.1;TP183
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本文编号：1548375