维普期刊资源整合服务平台（V6.5）

维普中文期刊产品整合服务

1篇 您的检索式：作者名="ZOU Quanyi"

选中清除导出查看参考文献查看引证文献引用追踪共1页首页上一页第1页下一页末页 /1 跳转

		题名	作者	年代	出处	被引量
	1	A Novel Robust Online Extreme Learning Machine for the Non-Gaussian Noise显示文摘Samples collected from most industrial processes have two challenges:one is contaminated by the non-Gaussian noise,and the other is gradually obsolesced.This feature can obviously reduce the accuracy and generalization of models.To handle these challenges,a novel method,named the robust online extreme learning machine(RO-ELM),is proposed in this paper,in which the least mean p-power criterion is employed as the cost function which is to boost the robustness of the ELM,and the forgetting mechanism is introduced to discard the obsolescence samples.To investigate the performance of the ROELM,experiments on artificial and real-world datasets with the non-Gaussian noise are performed,and the datasets are from regression or classification problems.Results show that the RO-ELM is more robust than the ELM,the online sequential ELM(OS-ELM) and the OSELM with forgetting mechanism(FOS-ELM).The accuracy and generalization of the RO-ELM models are better than those of other models for online learning.	GU Jun ZOU Quanyi DENG Changhui WANG Xiaojun	2023	Chinese Journal of Electronics2023，32，1：	0

返回顶部每页显示：

共1页首页上一页第1页下一页末页 /1 跳转

goolge学术搜索-维普

网站首页 | 关于我们 | 联系我们 | 产品服务 | 客服中心 | 广告服务 | 版权声明 | 网站联盟 | 友情链接 | 售卡网点

版权所有© 渝B2-20050021-1　渝公网安备 50019002500403号　违法和不良信息举报中心

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费