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A Kind of Extreme Learning Machine Based on Memristor Activation Function

  • Hanman Li
  • Lidan WangEmail author
  • ShuKai Duan
Conference paper
Part of the Proceedings in Adaptation, Learning and Optimization book series (PALO, volume 10)

Abstract

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.

Keywords

Extreme learning machine Memristance-charge function Regression and classification performances 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.The College of Electronic and Information EngineeringSouthwest UniversityChongqingChina
  2. 2.National and Local Joint Engineering Laboratory of Intelligent Transmission and Control TechnologySouthwest UniversityChongqingChina
  3. 3.Brain-Inspired Computing and Intelligent Control of Chongqing Key LabSouthwest UniversityChongqingChina

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