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.
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.
References
Mendel, J.M.: Uncertain Rule Based Fuzzy Logic Systems: Introduction and New Directions. Prentice-Hall, Upper Saddle River (2001)
Mendez, G.M., Cavazos, A., Soto, R., Leduc, L.: Entry Temperature Prediction of a Hot Strip Mill by Hybrid Learning Type-2 FLS. Jour. of Intell. and Fuz. Sys. 17, 583–596 (2006)
Jang, J.-S.R., Sun, C.-T.: Neuro-fuzzy Modeling and Control. The Proceedings of the IEEE 3, 378–406 (1995)
Mendez, G.M., 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, pp. 380–385 (2003)
Mendez, G.M., 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, pp. 529–533 (2003)
Mendez, G.M., Juarez, I.L.: Orthogonal-back Propagation Hybrid Learning Algorithm for Interval Type-1 Non-singleton Type-2 Fuzzy Logic Systems. WSEAS Trans. on Sys. 3-4, 212–218 (2005)
Mendez, G.M., De los Angeles Hernandez, M.: Hybrid Learning for Interval Type-2 Fuzzy Systems Based on Orthogonal Least-squares and Back-propagation Methods. Inf. Scien. 179, 2157–3146 (2009)
Mendez, G.M., Castillo, O.: Interval Type-2 TSK Fuzzy Logic Systems Using Hybrid Learning Algorithm. In: The IEEE international Conference on Fuzzy Systems, pp. 230–235 (2005)
Mendez, G.M., Juarez, I.L.: First-order Interval Type-2 TSK Fuzzy Logic Systems Using a Hybrid Learning Algorithm. WSEAS Trans. on Comp. 4, 378–384 (2005)
Mendez, G.M.: Interval Type-1 Non-singleton Type-2 TSK Fuzzy Logic Systems Using the Hybrid Training Method RLS-BP. In: Analysis and Design of Intelligent Systems Using Soft Computing Techniques, pp. 36–44. Springer, Heidelberg (2007)
Mendez, G.M., de los Angeles Hernandez, M.: Interval Type-2 ANFIS. In: Innovations in Hybrid Intelligent Systems, pp. 64–71. Springer, Heidelberg (2007)
Lee, D.Y., Cho, H.S.: Neural Network Approach to the Control of the Plate Width in Hot Plate Mills. International Joint Conference on Neural Networks 5, 3391–3396 (1999)
Taylor, B.N., Kuyatt, B.: Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results, NIST Technical Note 1297 (1994)
Wang, L.-X.: Fuzzy Systems are Universal Approximators. In: Proceedings of the IEEE Conf. on Fuzzy Systems, 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, 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 Systems 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)
GE Models, Users reference 1, Roanoke VA (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mendez, G.M., Hernández, A., Cavazos, A., Mata-Jiménez, MT. (2010). 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. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_52
Download citation
DOI: https://doi.org/10.1007/978-3-642-13769-3_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13768-6
Online ISBN: 978-3-642-13769-3
eBook Packages: Computer ScienceComputer Science (R0)