Open AccessExtreme Learning Machine Based on Calculating the Output Weight of Partial Robust M-regression
Author(s) -
Gongcheng Yao,
Gaitang Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1848/1/012148
Subject(s) - extreme learning machine , support vector machine , artificial intelligence , generalization , algorithm , computer science , regression , matrix (chemical analysis) , machine learning , online machine learning , mathematics , unsupervised learning , artificial neural network , statistics , mathematical analysis , materials science , composite material
In order to improve the nonlinear mapping capability and learning performance of extreme learning machine (ELM), a new learning algorithm called extreme learning machine based on calculating the output weight of partial robust M-regression is proposed. This algorithm introduces the partial robust M-regression into the extreme learning machine algorithm. Firstly, the hidden layer output matrix H is calculated by extreme learning machine. Secondly, the matrix H and vector Y are weighted by weighted strategy. Then, PRM algorithm is used to establish the regression model between the weighted matrix Hw and vector Yw , and calculate its regression coefficient, namely output weight of the ELM Algorithm. The proposed method predicts the Mackay’s robot arm regression and sediment concentration of the Yellow River Basin to verify the effectiveness of the method. The simulation results show that the proposed PRMELM algorithm is superior to the original extreme learning machine algorithm in prediction accuracy and generalization performance.