Online sequential fuzzy extreme learning machine for function approximation and classification problems.
In this correspondence, an online sequential fuzzy extreme learning machine (OS-Fuzzy-ELM) has been developed for function approximation and classification problems. The equivalence of a Takagi-Sugeno-Kang (TSK) fuzzy inference system (FIS) to a generalized single hidden-layer feedforward network is shown first, which is then used to develop the OS-Fuzzy-ELM algorithm. This results in a FIS that can handle any bounded nonconstant piecewise continuous membership function. Furthermore, the learning in OS-Fuzzy-ELM can be done with the input data coming in a one-by-one mode or a chunk-by-chunk (a block of data) mode with fixed or varying chunk size. In OS-Fuzzy-ELM, all the antecedent parameters of membership functions are randomly assigned first, and then, the corresponding consequent parameters are determined analytically. Performance comparisons 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.
Rong
Hai-Jun
HJ
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798.
Huang
Guang-Bin
GB
Sundararajan
N
N
Saratchandran
P
P