Type-1 non-singleton type-2 takagi-sugeno-kang fuzzy logic systems using the hybrid mechanism composed by a kalman type filter and back propagation methods

  • Authors:

  • Gerardo M. Mendez;Angeles Hernández;Alberto Cavazos;Marco-Tulio Mata-Jiménez

  • Affiliations:

  • Department of Electrical and Electronics Engineering, Instituto Tecnologico de Nuevo Leon, Cd Guadalupe, NL, Mexico;Department Bussineses Administration Sciences, Instituto Tecnologico de Nuevo Leon, Cd Guadalupe, NL, Mexico;Universidad Autónoma de Nuevo León, San Nicolas de los Garza, NL, Mexico;Universidad Autónoma de Nuevo León, San Nicolas de los Garza, NL, Mexico

  • Venue:
  • HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
  • Year:
  • 2010

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Abstract

This article presents a novel learning methodology based on the hybrid mechanism for training interval type-1 non-singleton type-2 Takagi-Sugeno-Kang fuzzy logic systems As reported in the literature, the performance indexes of these hybrid models have proved to be better than the individual training mechanism when used alone The proposed hybrid methodology was tested thru the modeling and prediction of the steel strip temperature at the descaler box entry as rolled in an industrial hot strip mill Results show that the proposed method compensates better for uncertain measurements than previous type-2 Takagi-Sugeno-Kang hybrid learning or back propagation developments.