An adaptive activation function for multilayer feedforward neural networks

Chien Cheng Yu, Yun Ching Tang, Bin-Da Liu

Research output: Contribution to conferencePaperpeer-review

13 Citations (Scopus)

Abstract

The aim of this paper is to propose a new adaptive activation function for multilayer feedforward neural networks. Based upon the back-propagation (BP) algorithm, an effective learning method is derived to adjust the free parameters in the activation function as well as the connected weights between neurons. Its performance is demonstrated by the N-parity and two-spiral problems. The simulation results showed that the proposed method is more suitable to the pattern classification problems and its learning speed is much faster than that of traditional networks with fixed activation function.

Original languageEnglish
Pages645-650
Number of pages6
Publication statusPublished - 2002 Dec 1
Event2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering - Beijing, China
Duration: 2002 Oct 282002 Oct 31

Other

Other2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering
CountryChina
CityBeijing
Period02-10-2802-10-31

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

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