吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (1): 195-203.doi: 10.13229/j.cnki.jdxbgxb20200876
• 计算机科学与技术 • 上一篇
Hong-kui XU1,2(),Tong-tong JIANG1,Xin LI1,Bin-xiang JIANG1,3,Yong-lei WANG4
摘要:
针对长期演进LTE网络上行干扰分类模型中噪声敏感、训练时间长的问题,建立了结合堆栈降噪自编码器与极限学习机的LTE网络上行干扰分析模型。使用上行干扰原始数据无监督地预训练堆栈降噪自编码(SDAE)提取高层抽象特征,并为极限学习机(ELM)分类器提供初始参数。该模型发挥了ELM收敛快和SDAE抑制噪声的优势,同时克服了ELM参数随机赋值造成的鲁棒性不足的问题。实验结果表明,该模型提高了LTE网络上行干扰分析的效率,并具有较强的鲁棒性。
中图分类号:
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