Title: Interval type-1 non-singleton type-2 fuzzy logic systems are type-2 adaptive neuro-fuzzy inference systems
Authors: Gerardo M. Mendez, Ma. De Los Angeles Hernandez
Addresses: Instituto Tecnologico de Nuevo Leon, Department of Electronics and Electrical Engineering, Av. Eloy Cavazos 2001, Cd. Guadalupe, NL, CP 67170, Mexico. ' Instituto Tecnologico de Nuevo Leon, Department of Economic Sciences, Av. Eloy Cavazos 2001, Cd. Guadalupe, NL, CP 67170, Mexico
Abstract: This article presents 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.
Keywords: interval type-2 fuzzy logic systems; IT2 FLS; ANFIS; neuro-fuzzy systems; hybrid learning; neural networks; recursive least squares; RLS; TSK fuzzy modelling; steel strip temperature; hot strip mills; HSM; transfer bar temperature; surface temperature; finishing scale breaker.
International Journal of Reasoning-based Intelligent Systems, 2010 Vol.2 No.2, pp.95 - 99
Available online: 30 Aug 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article