Design and identify tubercle bacilli diagnosis system with TSK-type neuro fuzzy controllers

Hsien Tse Chen*, Sheng-Fuu Lin, Yung Chi Hsu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This paper proposes a TSK-type Neuro Fuzzy Controllers (TFC) with a group interaction-based evolutionary algorithm (GIEA) for constructing the tubercle bacilli diagnosis system (TBDS). The proposed GIEA is designed basing on symbiotic evolution which each chromosome in the population represents only partial solution. The whole solution consists of several chromosomes. The GIEA is different from the traditional symbiotic evolution. Each population in the GIEA is divided into several groups. Each group represents a set of the chromosomes that belong to only one fuzzy rule. Moreover, in the GIEA,the interaction ability is considered that the chromosomes will interact with other groups to generate the better chromosomes by elites-base interaction crossover strategy (EICS). In the TBDS, the EICS is used to train the TBDS. After trained by the EICS, the TBDS can diagnose the visible tubercle bacilli. The performance of the GIEA achieves better than other existing models in tubercle bacilli.

Original languageEnglish
Pages (from-to)3719-3726
Number of pages8
JournalIndian Journal of Science and Technology
Volume5
Issue number12
DOIs
StatePublished - 1 Dec 2012

Keywords

  • Neuro Fuzzy controllers
  • Reinforcement learning
  • Symbiotic evolution
  • Tubercle bacilli

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