Recursive SVD-based Fuzzy Extreme Learning Machine

We propose a recursive singular value decomposition (SVD)-based fuzzy extreme learning machine (RSVD-F-ELM) for the online learning in classification or regression analysis. By adopting the same architecture and operation as fuzzy extreme learning machine (F-ELM), which is originally designed for the batch learning, and replacing the Moore Penrose generalized inverse in F-ELM with a recursive SVD-based least squares estimator for optimizing the output weights recursively, RSVD-F-ELM is applicable for the online learning. Compared with the other online learning approach, namely online sequential fuzzy extreme learning machine (OS-F-ELM), experimental results have revealed that RSVD-F-ELM generates the larger accuracy rates in classification analysis and the smaller mean squared errors in regression analysis. Moreover, the learning stability of RSVD-F-ELM is much better.