Fusion algorithm based on fuzzy neural networks

Alexey Natekin, Alois Knoll

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

Abstract

The problem of optimal fusion of several predictive machine learning regression models is considered. The method of combining different predictive models based on additive fuzzy systems is presented. The framework of model fusion based on fuzzy neural networks is described and the appropriate algorithms are derived. Learning process justifications and the requirement of the separate fusion set are discussed. The presented models are supported with the real world application example of robotic hand control.

Original languageEnglish
Title of host publication2013 IEEE 8th International Symposium on Intelligent Signal Processing, WISP 2013 - Proceedings
PublisherIEEE Computer Society
Pages43-47
Number of pages5
ISBN (Print)9781467345439
DOIs
StatePublished - 2013
Event2013 IEEE 8th International Symposium on Intelligent Signal Processing, WISP 2013 - Funchal, Madeira, Portugal
Duration: 16 Sep 201318 Sep 2013

Publication series

Name2013 IEEE 8th International Symposium on Intelligent Signal Processing, WISP 2013 - Proceedings

Conference

Conference2013 IEEE 8th International Symposium on Intelligent Signal Processing, WISP 2013
Country/TerritoryPortugal
CityFunchal, Madeira
Period16/09/1318/09/13

Keywords

  • data fusion
  • fuzzy associative memory
  • machine learning
  • model fusion
  • predictive ensembles

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