A Kind of Extreme Learning Machine Based on Memristor Activation Function

被引:0
|
作者
Li, Hanman [1 ,2 ,3 ]
Wang, Lidan [1 ,2 ,3 ]
Duan, ShuKai [1 ,2 ,3 ]
机构
[1] Southwest Univ, Coll Elect & Informat Engn, Chongqing 400415, Peoples R China
[2] Southwest Univ, Natl & Local Joint Engn Lab Intelligent Transmiss, Chongqing 400415, Peoples R China
[3] Southwest Univ, Brain Inspired Comp & Intelligent Control Chongqi, Chongqing 400415, Peoples R China
来源
PROCEEDINGS OF ELM-2017 | 2019年 / 10卷
基金
中国国家自然科学基金;
关键词
Extreme learning machine; Memristance-charge function; Regression and classification performances;
D O I
10.1007/978-3-030-01520-6_19
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Extreme learning machine is a new type of algorithm for single hidden layer feedforward neural network. Compared with the traditional algorithms, ELM avoids long time iteration and has the advantages of high speed, small errors. Among them, the activation function plays an important role in the system. Whereas the general ELM usually uses Sigmod function as the activation function, a new kind ELM using memristor's memristance-charge function as activation function is proposed in this article. Experiments show that, compared with the ELM and the traditional neural network algorithms, the extreme learning machine based on memristance-charge activation function can shorten the time and improve the accuracy. In a word, it has better classification and regression performances.
引用
收藏
页码:210 / 218
页数:9
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