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机译:神经网络和和声搜索法对癫痫脑电信号的最佳分类
College of Information Engineering, Shanghai Maritime University, China,Department of Automation and Systems Technology, Aalto University School of Electrical Engineering, Finland;
Laboratory of Image Processing and Pattern Recognition, Beijing Normal University, China,School of Foundational Education, Peking University Health Science Center, China;
Department of Biomedical Engineering, Tampere University of Technology, Finland;
Laboratory of Image Processing and Pattern Recognition, Beijing Normal University, China;
Department of Automation and Systems Technology, Aalto University School of Electrical Engineering, Finland;
Laboratory of Image Processing and Pattern Recognition, Beijing Normal University, China;
Department of Automation and Systems Technology, Aalto University School of Electrical Engineering, Finland;
Harmony Search (HS) method; ElectroEncephaioGram (EEG); BP neural networks; optimization; Opposition-Based Learning (OBL); memetic computing; bee foraging algorithm; signal classification;
机译:神经网络和和声搜索法对癫痫脑电信号的最佳分类
机译:神经网络和和声搜索法对癫痫脑电信号的最佳分类
机译:使用时延神经网络和概率神经网络对癫痫性脑电信号进行分类
机译:BP神经网络与基于和谐搜索方法的癫痫EEG信号分类训练
机译:使用卷积神经网络对脑电信号中癫痫样瞬变进行检测和分类
机译:基于深度卷积神经网络的癫痫脑电图(EEG)信号分类
机译:使用时延神经网络和概率神经网络对癫痫性脑电信号进行分类