摘要
在宽度学习系统的基础上,以误差矢量的p-范数为损失函数,结合固定点迭代策略,提出基于最小p-范数的宽度学习系统.通过灵活设置p的取值(p≥1),提出的最小p-范数宽度学习系统能较好应对不同噪声的干扰,实现对不确定数据的建模任务.数值实验表明,在高斯、均匀、脉冲噪声干扰环境下,文中系统均能保持良好性能.将该系统应用于脑电图分类任务,在大多数被试上都能取得较高的分类精度.
Based on the broad learning system( BLS),a least p-norm based BLS( LP-BLS) is proposed,and it takes the p-norm of error vector as loss function and combines the fixed-point iteration strategy. With the proposed LP-BLS,the interferences from different noises can be well dealt with by flexibly setting the value of p( p≥1),so that the modeling task of unknown data can be better completed.Numerical experiments show that the good performance of the proposed method can always be maintained with Gaussian noise, uniform noise and impulse noise. Finally, the system is applied to electroencephalogram( EEG) classification task and achieves a higher classification accuracy on most subjects.
作者
郑云飞
陈霸东
ZHENG Yunfei;CHEN Badong(Institute of Artificial Intelligence and Robotics,School of Electronic and Information Engineering,Xi’an Jiaotong University,Xi'an 710049)
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2019年第1期51-57,共7页
Pattern Recognition and Artificial Intelligence
基金
国家重点基础研究发展计划(973计划)项目(No.2015CB351703)
国家自然科学基金项目(No.91648208)资助~~
关键词
宽度学习系统
最小p-范数
固定点迭代
脑电图分类
Broad Learning System
Least p-Norm
Fixed-Point Iteration
Electroencephalogram(EEG) Classification
作者简介
郑云飞,博士研究生,主要研究方向为机器学习、脑机接口.E-mail:zhengyf@stu.xjtu.edu.cn;通讯作者:陈霸东,博士,教授,主要研究方向为信号处理、机器学习、脑机接口.E-mail:chenbd@mail.xjtu.edu.cn.
