Interval Type-1 Non-singleton Type-2 TSK Fuzzy Logic Systems Using the Hybrid Training Method RLS-BP

  • G. M. Mendez
Chapter
Part of the Advances in Soft Computing book series (AINSC, volume 41)

Abstract

This paper describes a new learning methodology based on a hybrid algorithm for interval type-1 non-singleton type-2 TSK fuzzy logic systems (FLS). Using input-output data pairs during the forward pass of the training process, the interval type-1 non-singleton type-2 TSK FLS output is calculated and the consequent parameters are estimated by the recursive least-squares (RLS) method. In the backward pass, the error propagates backward, and the antecedent parameters are estimated by the back-propagation (BP) method. The proposed hybrid methodology was used to construct an interval type-1 non-singleton type-2 TSK fuzzy model capable of approximating the behaviour of the steel strip temperature as it is being rolled in an industrial Hot Strip Mill (HSM) and used to predict the transfer bar surface temperature at finishing Scale Breaker (SB) entry zone. Comparative results show the performance of the hybrid learning method (RLS-BP) against the only BP learning method.

Keywords

Fuzzy System Fuzzy Logic System Scale Breaker Consequent Parameter Finish Mill 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • G. M. Mendez
    • 1
  1. 1.Departamento de Investigacion y Desarrollo de Aplicaciones, Intelligent Systems, SA de CV, Vista Aurora 320, Col. Linda Vista, Cd. Guadalupe, NLMéxico

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