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 behavior 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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Mendel, J.M.: Uncertain Rule Based Fuzzy Logic Systems: Introduction and New Directions. Prentice-Hall, Upper Saddle River (2001)
Mendez, G., Cavazos, A., Leduc, L., Soto, R.: Hot Strip Mill Temperature Prediction Using Hybrid Learning Interval Singleton Type-2 FLS. In: Proceedings of the IASTED International Conference on Modeling and Simulation, Palm Springs, February 2003, pp. 380–385 (2003)
Mendez, G., Cavazos, A., Leduc, L., Soto, R.: Modeling of a Hot Strip Mill Temperature Using Hybrid Learning for Interval Type-1 and Type-2 Non-Singleton Type-2 FLS. In: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, Benalmádena, Spain, September 2003, pp. 529–533 (2003)
Mendez, G., Juarez, I.: Orthogonal-Back Propagation Hybrid Learning Algorithm for Interval Type-1 Non-Singleton Type-2 Fuzzy Logic Systems. WSEAS Transactions on Systems 4(3) (March 2005), ISSN 1109-2777
Mendez, G., Castillo, O.: Interval Type-2 TSK Fuzzy Logic Systems Using Hybrid Learning Algorithm. In: FUZZ-IEEE 2005 The international Conference on Fuzzy Systems, Reno Nevada, USA, pp. 230–235 (2005)
Lee, D.Y., Cho, H.S.: Neural Network Approach to the Control of the Plate Width in Hot Plate Mills. In: International Joint Conference on Neural Networks, vol. 5, pp. 3391–3396 (1999)
Taylor, B.N., Kuyatt, C.E.: Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results, NIST Technical Note 1297 (September 1994)
Wang, L.-X.: Fuzzy Systems are Universal Approximators. In: Proceedings of the IEEE Conf. On Fuzzy Systems, San Diego, pp. 1163–1170 (1992)
Wang, L.-X., Mendel, J.M.: Back-Propagation Fuzzy Systems as Nonlinear Dynamic System Identifiers. In: Proceedings of the IEEE Conf. On Fuzzy Systems, San Diego, CA, March 1992, pp. 1409–1418 (1992)
Wang, L.-X.: Fuzzy Systems are Universal Approximators. In: Proceedings of the IEEE Conf. on Fuzzy Systems, San Diego, pp. 1163–1170 (1992)
Wang, L.-X.: A Course in Fuzzy Systems and Control. Prentice Hall PTR, Upper Saddle River (1997)
Liang, Q.J., Mendel, J.M.: Interval type-2 fuzzy logic systems: Theory and design. Trans. Fuzzy Sistems. 8, 535–550 (2000)
Jang, J.-S.R., Sun, C.-T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice-Hall, Upper Saddle River (1997)
Jang, J.-S.R., Sun, C.-T.: Neuro-Fuzzy Modeling and Control. The Proceedings of the IEEE 3, 378–406 (1995)
GE Models, Users reference, Roanoke VA, vol. 1 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Mendez, G.M., De Los Angeles Hernandez, M. (2009). Hybrid Interval Type-1 Non-singleton Type-2 Fuzzy Logic Systems Are Type-2 Adaptive Neuro-fuzzy Inference Systems. In: Castillo, O., Pedrycz, W., Kacprzyk, J. (eds) Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control. Studies in Computational Intelligence, vol 257. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04514-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-04514-1_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04513-4
Online ISBN: 978-3-642-04514-1
eBook Packages: EngineeringEngineering (R0)