标题：The ELM Learning Algorithm with Tunable Activation Functions and Its Application
作者：Li Bin; Li Yibin; Rong Xuewen
作者机构：[Li Bin; Li Yibin; Rong Xuewen] Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Peoples R China.
会议名称：30th Chinese Control Conference
会议日期：JUL 22-24, 2011
来源：2011 30TH CHINESE CONTROL CONFERENCE (CCC)
关键词：Extreme Learning Machine; Single Hidden Layer Feed-forward Neural; Networks; Tunable Activation Function; Differential Evolution Algorithm
摘要：Based on the problem dependency of activation functions with Extreme Learning Machine (ELM) learning algorithm, the ELM learning algorithm with tunable activation functions is proposed in this paper. The presented algorithm determines its activation functions dynamically with differential evolution algorithm based on the input training data of problem. Compared with ELM and E-ELM learning algorithms for benchmark problems of function approximation and pattern classification, the simulation results show that the proposed algorithm can provide better generalization performance and robustness with the same network size and compact network structure.