A hybrid learning method composed by the orthogonal least-squares and the back-propagation learning algorithms for interval A2-C1 type-1 non-singleton type-2 TSK fuzzy logic systems
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| 024 | 7 | 0 | |a 10.1007/s00500-014-1287-8 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s00500-014-1287-8 | ||
| 245 | 0 | 2 | |a A hybrid learning method composed by the orthogonal least-squares and the back-propagation learning algorithms for interval A2-C1 type-1 non-singleton type-2 TSK fuzzy logic systems |h [Elektronische Daten] |c [María de los Angeles Hernandez, Patricia Melin, Gerardo Méndez, Oscar Castillo, Ismael López-Juarez] |
| 520 | 3 | |a The purpose of this paper is to present a hybrid learning method for interval A2-C1 type-1 non-singleton type-2 TSK fuzzy logic system that uses the recursive orthogonal least-squares algorithm to tune the type-1 consequent parameters, and the back-propagation algorithm to tune the interval type-2 antecedent parameters. Based on the combination of these two training algorithms the new hybrid learning method changes the interval type-2 fuzzy model parameters adaptively and minimizes the proposed error function as the new type-1 non-singleton input-output data pairs are processed. Its antecedent sets are interval type-2 fuzzy sets, its consequent sets are type-1 fuzzy sets, and its inputs are type-1 non-singleton fuzzy numbers with uncertain standard deviations. Comparison with the non-hybrid interval A2-C1 type-1 non-singleton type-2 Takagi-Sugeno-Kang fuzzy logic system that only uses the back-propagation algorithm for both antecedent and consequent parameter's adaptation demonstrates that the proposed hybrid algorithm is a well-performing nonlinear adaptation that enables the interval type-2 fuzzy model to optimally match the nonlinear behavior of the process. The application of the interval type-2 fuzzy logic as adaptable predictor using the proposed hybrid learning method was constructed for the modeling and prediction of the transfer bar surface temperature in an industrial hot strip mill facility. Experimental results demonstrated that this method improves the temperature prediction performance of the interval A2-C1 type-1 non-singleton type-2 Takagi-Sugeno-Kang fuzzy logic system. | |
| 540 | |a Springer-Verlag Berlin Heidelberg, 2014 | ||
| 690 | 7 | |a Hybrid learning method |2 nationallicence | |
| 690 | 7 | |a Fuzzy intelligent prediction |2 nationallicence | |
| 690 | 7 | |a Interval type-2 fuzzy logic for system identification |2 nationallicence | |
| 690 | 7 | |a Nonlinear industrial process modeling and prediction |2 nationallicence | |
| 700 | 1 | |a de los Angeles Hernandez |D María |u Departamento de Ciencias Económico-Administrativas, Instituto Tecnológico de Nuevo León, CP67170, Cd. Guadalupe, Mexico |4 aut | |
| 700 | 1 | |a Melin |D Patricia |u Division of Graduate Studies and Research, Instituto Tecnológico de Tijuana, Tijuana, BC, Mexico |4 aut | |
| 700 | 1 | |a Méndez |D Gerardo |u Departamento de Ingeniería Eléctrica y Electrónica, Instituto Tecnológico de Nuevo León, CP67170, Cd. Guadalupe, Mexico |4 aut | |
| 700 | 1 | |a Castillo |D Oscar |u Division of Graduate Studies and Research, Instituto Tecnológico de Tijuana, Tijuana, BC, Mexico |4 aut | |
| 700 | 1 | |a López-Juarez |D Ismael |u Centro de Manufactura Avanzada, Centro de Investigaciones y Estudios Avanzados del IPN Unidad Saltillo, Ramos Arispe, Coahuila, Mexico |4 aut | |
| 773 | 0 | |t Soft Computing |d Springer Berlin Heidelberg |g 19/3(2015-03-01), 661-678 |x 1432-7643 |q 19:3<661 |1 2015 |2 19 |o 500 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s00500-014-1287-8 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s00500-014-1287-8 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a de los Angeles Hernandez |D María |u Departamento de Ciencias Económico-Administrativas, Instituto Tecnológico de Nuevo León, CP67170, Cd. Guadalupe, Mexico |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Melin |D Patricia |u Division of Graduate Studies and Research, Instituto Tecnológico de Tijuana, Tijuana, BC, Mexico |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Méndez |D Gerardo |u Departamento de Ingeniería Eléctrica y Electrónica, Instituto Tecnológico de Nuevo León, CP67170, Cd. Guadalupe, Mexico |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Castillo |D Oscar |u Division of Graduate Studies and Research, Instituto Tecnológico de Tijuana, Tijuana, BC, Mexico |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a López-Juarez |D Ismael |u Centro de Manufactura Avanzada, Centro de Investigaciones y Estudios Avanzados del IPN Unidad Saltillo, Ramos Arispe, Coahuila, Mexico |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Soft Computing |d Springer Berlin Heidelberg |g 19/3(2015-03-01), 661-678 |x 1432-7643 |q 19:3<661 |1 2015 |2 19 |o 500 | ||