Multitarget prediction using an aim-object-based asymmetric neuro-fuzzy system: A novel approach

Chia Hao Tu, Chunshien Li

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This paper proposes an aim-object-based asymmetric neuro-fuzzy system that is different from conventional models in two ways. First, this system has an asymmetric structure with different numbers of neurons in the premise and consequent layers. Secondly, with the assistance of the sphere complex fuzzy set, depending on the application, our model can alter the number of outputs. In addition, a hybrid learning algorithm combining the whale optimization algorithm and the recursive least-square estimator is proposed to optimize the proposed model. The results of the experiment show that the proposed model can simultaneously predict multiple targets with fewer parameters and maintain a performance level similar to that of the conventional neuro-fuzzy system.

Original languageEnglish
Pages (from-to)155-169
Number of pages15
JournalNeurocomputing
Volume389
DOIs
StatePublished - 14 May 2020

Keywords

  • Aim-object layer
  • Aim-object-based asymmetric
  • Hybrid learning algorithm
  • Multitarget prediction
  • Neuro-fuzzy system
  • Sphere complex fuzzy set

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