Design and Analysis of Neural Networks Based on Linearly Translated Features

Jiasen Wang, Jun Wang, Wei Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, neural networks based on linearly translated features (LTFs) are presented. LTFs including uniform, non-uniform, and multiple translation vectors are embedded into feedforward neural networks. Learning algorithms are presented for the neural networks. Learning capabilities of the neural networks are analyzed. Experimental results on approximation' identification, and evaluation problems are reported to substantiate the efficacy of the neural networks and learning algorithms.

Original languageEnglish
Title of host publication10th International Conference on Intelligent Control and Information Processing, ICICIP 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages289-296
Number of pages8
ISBN (Electronic)9781728100159
DOIs
Publication statusPublished - Dec 2019
Externally publishedYes
Event10th International Conference on Intelligent Control and Information Processing, ICICIP 2019 - Marrakesh, Morocco
Duration: 14 Dec 201919 Dec 2019

Publication series

Name10th International Conference on Intelligent Control and Information Processing, ICICIP 2019

Conference

Conference10th International Conference on Intelligent Control and Information Processing, ICICIP 2019
Country/TerritoryMorocco
CityMarrakesh
Period14/12/1919/12/19

Keywords

  • linearly translated features
  • Neural networks

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