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.
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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
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DOI: https://doi.org/10.1007/978-3-642-13769-3_52
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