关键词: 极限学习机（ELM）, 大数据, 有序加权平均算子

Abstract: Under the background of huge data input, a partitioned Extreme Learning Machine（ELM） algorithm is proposed to improve the learning speed of the ELM and reduce the memory consumption of computers. The huge data are divided into [K] equal parts, and weights of each ELM are trained based on each part data. The comprehensive weight  of the partitioned ELM is determined based on the arithmetic average operator. To avoid the abnormal data influencing the output of ELM, Ordered Weighted Averaging（OWA） operators are used to fuse the output information of each ELM, so that the output of partitioned ELM is more stable. Numerical simulation shows that the accuracy, the learning speed  and the maximum memory consumption of the partitioned ELM are higher than that of the traditional ELM, it verifies the feasibility and rationality of the proposed method.

Key words: Extreme Learning Machines（ELM）, big data, ordered weighted averaging operators