基于最小p-范数的宽度学习系统

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.