奇异值分解下在线鲁棒正则化随机网络OA北大核心CSTPCD

Online robust random vector functional link network(OR-RVFLN)has better approximation,faster conver-gence speed,higher robustness and smaller storage space.However,the OR-RVFLN algorithm can cause the ill-posed problem of the matrix in the calculation process,which makes the low precision of the hidden layer output matrix.To solve this problem,based on the singular value decomposition approach,this paper proposes the online robust regularized random vector functional link network(SVD-OR-RRVFLN).Firstly,the SVD-OR-RRVFLN introduces the regularization term into the OR-RVFLN algorithm,and the singular value decomposition approach is used for the hidden layer output matrix.Further,the kernel density estimation(KDE)method is used to update the matrix weight.Secondly,the necessity and convergence of the proposed algorithm are analyzed.Finally,the proposed method is applied to Benchmark data set and the index prediction of grinding particle size.The experimental results show that the proposed algorithm can not only effectively improve the prediction accuracy and robustness of the model,but also have faster training speed.