Abstract: ELM for batch learning developed by Huang et al has been shown to be extremely fast with generalization performance better than other batch training methods．In order to lean online,a pruned incremental extreme leaning algorithm was developed for fuzzy neural network．Based on the previously proposed ELM method,we proposed a pruned incremental extreme leaning algorithm．First a set of simple antecedents and random values for the parameters of input membership functions were randomly generated.Then SVD was used to rank the fuzzy basis functions．Then the best number of fuzzy rules was selected by performing a fast computation of the leave-one-out validation error．Finally,the consequents parameters were determined analytically．During the procedure of learning,it is not need to memory the last date．It is a real incremental leaning method．When a new data arrives,it is not need to retrain the network．A comparison was performed against well known neuro-fuzzy methods．It is shown that the method proposed is robust and competitive in terms of accuracy and speed.

Key words: Extreme leaning machine(ELM),Incremental earning algorithm,Fuzzy neural netwok(FNN),Radial basis function