Online Sequential Fuzzy Extreme Learning Machine

In this chapter, a fuzzy extreme learning machine (Fuzzy-ELM) developed based on the ELM and the functional equivalence between the FNN and the fuzzy inference system is first introduced. In Fuzzy-ELM, all the antecedent parameters of the membership functions are randomly assigned and then the corresponding consequent parameters are determined analytically. To suit the realtime applications, the online sequential Fuzzy-ELM (OS-Fuzzy-ELM) algorithm is further developed. The learning in OS-Fuzzy-ELM can be done with the input data coming in a one-by-one mode or chunk-by-chunk (a block of data) mode with fixed or varying chunk size. Performance comparison of OS-Fuzzy-ELM with other existing algorithms are presented using real world benchmark problems in the areas of nonlinear system identification, regression and classification. The results show that the proposed OS-Fuzzy-ELM produces similar or better accuracies with at least an order of magnitude reduction in the training time.