An Efficient Extreme Learning Machine Based on Fuzzy Information Granulation

International Association of Online Engineering

Bo-hua WANG¹  Jun-peng CHEN¹  Lei LIU³  Xia-fu LV³  Yong WANG³ 

[1] Network Control ,MOE ,Chongqing University of Posts and Telecom;College of Mobile Telecommunications. Chongqing University of Posts and Telecom;Key Laboratory of Industrial Internet of Things &

关键词: Extreme learning machine (ELM);    Fuzzy information granulation (FIG)，Neural networks;    Support vector machine (SVM);   

DOI  :  

学科分类：社会科学、人文和艺术（综合）

来源: International Association of Online Engineering

【 摘 要 】

In order to improve learning efficiency and generalization ability of extreme learning machine (ELM), an efficient extreme learning machine based on fuzzy information granulation (FIG) is put forward. Firstly, using FIG to get rid of redundant information in the original data set and then ELM is used to do train granulated data for prediction. This method not only improves the speed of basic ELM algorithm that contains many hidden nodes, but also overcomes the weakness of basic ELM of low learning efficiency and generalization ability by getting rid of redundant information in the observed values. The experimental results show that the proposed method is effective and can produce desirable generalization performance in most cases based on a few regression and classification problem.

【 授权许可】

Unknown