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| Least p-Norm Based Broad Learning System |
| ZHENG Yunfei1, CHEN Badong1 |
| 1.Institute of Artificial Intelligence and Robotics, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049 |
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Abstract 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.
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Received: 18 October 2018
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| Fund:Supported by National Basic Research Program of China(973 Program)(No.2015CB351703), National Natural Science Founda-tion of China(No.91648208) |
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Corresponding Authors:
CHEN Badong, Ph.D., professor. His research interests include signal processing, machine learning and brain computer interface.
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| About author:: ZHENG Yunfei, Ph.D.candidate. His research interests include machine learning and brain computer interface. |
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2019, Vol. 32 
