Extreme learning machine on robust estimation
Yihan Hu · Jisuanji yingyong yanjiu · 2012
Extreme learning machine(ELM) is a kind of single-hidden layer feedforword neural networks(SLFNs).Comparing with traditional neural network algorithms,it is simpler in structure,with higher learning speed,and good generalization performance.The output-weight of ELM was calculated by LSE(least square estimation) method.However,LSE lack of robustness,the result would be seriously damaged when there were outliers in the training data.In order to solve this problem,this paper derived a novel approach based on M-estimators of extreme learning machine called RBELM.Simulation results indicate that the RBELM proposed can significantly robust against data noise and outliers.