Extreme learning machine on robust estimation
一种基于鲁棒估计的极限学习机方法

Extreme learning machineELM is a kind of single-hidden layer feedforword neural networksSLFNs. 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 LSEleast 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.