Least p-Norm Based Broad Learning System

被引：0
|
作者：
Zheng Y. [1 ]
Chen B. [1 ]
机构：
[1] Institute of Artificial Intelligence and Robotics, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2019年 / 32卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Broad Learning System; Electroencephalogram(EEG) Classification; Fixed-Point Iteration; Least p-Norm;
D O I：
10.16451/j.cnki.issn1003-6059.201901007
中图分类号：
学科分类号：
摘要：
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. © 2019, Science Press. All right reserved.
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页码：51 / 57
页数：6
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