中文摘要：

极限学习机（Extreme Learning Machine,ELM）是一种单隐层前馈神经网络（Single-hidden Layer Feedforward Neural Networks,SLFN）,它相较于传统神经网络算法来说结构简单,具有较快的学习速度,以及良好的泛化性能等优点。由最小二乘法（Least Square,LE）计算得出的输出权值,往往由于设计矩阵为奇异矩阵,得到的权值有较大偏差,遇到有噪声的数据时,算法的鲁棒性无法保证。主成分估计是对最小二乘估计的一种改进算法,主成分估计能有效的改善设计矩阵奇异造成的影响,能有效的提高网络模型的鲁棒性和抗噪能力。提出了一种基于主成分估计的极限学习机方法（PC-ELM）,实验结果表明,此方法能有效提高算法的鲁棒性和泛化能力。

英文摘要：

Extreme Learning Machine（ELM） is a kind of Single-hidden Layer Feedforword Neural Networks（SLFN）. It is simpler in structure and faster with learning speed Comparing to traditional neural network algorithms. The output-weight of ELM is calculated by least square estimation. ELM may produce a poor and unreliable solution when the hidden layer output matrix is not full column rank,or when the training data is contaminated with outliers. Principal components estimation is an improved algorithm for least square estimation. The principal components estimation can significantly improve the robustness against data noise and outliers when the hidden layer outpu tmatrix is not full column rank. A novel approach based on principal components estimation of extreme learning machine called PC-ELM is derived in this paper. Simulation results indicate that the proposed algorithm in this paper can significantly improve the robustness against data noise and outliers as well as good generalization performance.