کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
411648 | 679578 | 2016 | 9 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Cholesky factorization based online regularized and kernelized extreme learning machines with forgetting mechanism Cholesky factorization based online regularized and kernelized extreme learning machines with forgetting mechanism](/preview/png/411648.png)
In this paper, we propose two alternative schemes of fast online sequential extreme learning machine (ELM) for training the single hidden-layer feedforward neural networks (SLFN), termed as Cholesky factorization based online regularized ELM with forgetting mechanism (CF-FORELM) and Cholesky factorization based online kernelized ELM with forgetting mechanism (CF-FOKELM). First, the solutions of regularized ELM (RELM) and kernelized ELM (KELM) using the matrix Cholesky factorization are introduced; then the recursive method for calculating Cholesky factor of involved matrix in RELM and KELM is designed when RELM and KELM are applied to train SLFN online; consequently, the CF-FORELM and CF-FOKELM are obtained. The numerical simulation results show CF-FORELM demands less computational burden than Dynamic Regression ELM (DR-ELM), and CF-FOKELM also owns higher computational efficiency than both FOKELM and online sequential ELM with kernels (OS-ELMK), and CF-FORELM is less sensitive to model parameters than CF-FOKELM.
Journal: Neurocomputing - Volume 174, Part B, 22 January 2016, Pages 1147–1155