The Experts below are selected from a list of 138 Experts worldwide ranked by ideXlab platform

Gerardo M Mendez - One of the best experts on this subject based on the ideXlab platform.

  • modelling and control of coiling Entry Temperature using interval type 2 fuzzy logic systems
    Ironmaking & Steelmaking, 2010
    Co-Authors: Gerardo M Mendez, Rafael Colas, L Leduclezama, G Murilloperez, Jorge Ramirezcuellar, Jose J Lopez
    Abstract:

    The set-up of the cooling water applied to the strip as it traverses the runout table in order to achieve the coiler Entry Temperature was made by an intelligent model implemented using interval type-2 fuzzy logic systems. The model uses as inputs the targets for coiling Entry Temperature, strip thickness, finish mill exit Temperature and finishing mill exit speed. The experiments of this application were carried out for three different types of coil in a real hot strip mill. The results proved the feasibility of the system developed for coiler Entry Temperature prediction. Comparison with the online type-1 fuzzy logic based model shows that the proposed interval type-2 fuzzy logic system improves performance in coiler Entry Temperature prediction under the tested condition.

  • application of interval type 2 fuzzy logic systems for control of the coiling Entry Temperature in a hot strip mill
    Hybrid Artificial Intelligence Systems, 2009
    Co-Authors: Gerardo M Mendez, Rafael Colas, L Leduclezama, G Murilloperez, Jorge Ramirezcuellar, Jose J Lopez
    Abstract:

    An interval type-2 fuzzy logic system is used to setup the cooling water applied to the strip as it traverses the run out table in order to achieve the coiler Entry Temperature target. The interval type-2 fuzzy setup model uses as inputs the target coiling Entry Temperature, the target strip thickness, the predicted finish mill exit Temperature and the target finishing mill exit speed. The experimental results of the application of the interval type-2 fuzzy logic system for coiler Entry Temperature prediction in a real hot strip mill were carried out for three different types of coils. They proved the feasibility of the systems developed here for coiler Entry Temperature prediction. Comparison with an on-line type-1 fuzzy logic based model shows that the interval type-2 fuzzy logic system improves performance in coiler Entry Temperature prediction under the tested condition.

  • hybrid learning for interval type 2 fuzzy logic systems based on orthogonal least squares and back propagation methods
    Information Sciences, 2009
    Co-Authors: Gerardo M Mendez, M De Los Angeles Hernandez
    Abstract:

    This paper presents a novel learning methodology based on a hybrid algorithm for interval type-2 fuzzy logic systems. Since only the back-propagation method has been proposed in the literature for the tuning of both the antecedent and the consequent parameters of type-2 fuzzy logic systems, a hybrid learning algorithm has been developed. The hybrid method uses a recursive orthogonal least-squares method for tuning the consequent parameters and the back-propagation method for tuning the antecedent parameters. Systems were tested for three types of inputs: (a) interval singleton, (b) interval type-1 non-singleton, and (c) interval type-2 non-singleton. Experiments were carried out on the application of hybrid interval type-2 fuzzy logic systems for prediction of the scale breaker Entry Temperature in a real hot strip mill for three different types of coil. The results proved the feasibility of the systems developed here for scale breaker Entry Temperature prediction. Comparison with type-1 fuzzy logic systems shows that hybrid learning interval type-2 fuzzy logic systems provide improved performance under the conditions tested.

  • Entry Temperature prediction of a hot strip mill by a hybrid learning type 2 fls
    Journal of Intelligent and Fuzzy Systems, 2006
    Co-Authors: Gerardo M Mendez, Alberto Cavazos, Rogelio Soto, L A Leduc
    Abstract:

    In Hot Strip Mills, on-line estimation of rolling variables is of crucial importance in order to Set-Up the Finishing Mill, i.e. setting initial working references for the in-bar regulators, and hence fulfilling quality requirements. This paper presents the experimental results of the application of type-2 fuzzy logic systems for scale breaker Entry Temperature prediction in a real hot strip mill. Since in the literature only back-propagation has been proposed for type-2 fuzzy logic systems, a hybrid learning algorithm has been developed. Such algorithm is also presented. The algorithm uses back-propagation with recursive least-squares and back propagation with square-root filter methods. The systems were tested for three types of inputs: a interval singleton b interval type-1 non-singleton, c interval type-2 non-singleton. The experiments were carried out for three different types of coils. Experimental results show the feasibility of the systems developed here for scale breaker Entry Temperature prediction. Comparison with type-1 fuzzy logic systems shows the hybrid learning type-2 fuzzy logic systems improve performance in scale breaker Entry Temperature prediction under the tested condition.

Alberto Cavazos - One of the best experts on this subject based on the ideXlab platform.

  • Fuzzy C-means Rule Generation for Fuzzy Entry Temperature Prediction in a Hot Strip Mill
    Journal of Iron and Steel Research International, 2016
    Co-Authors: José Angel Barrios, César Villanueva, Alberto Cavazos, Rafael Colas
    Abstract:

    Variable estimation for finishing mill set-up in hot rolling is greatly affected by measurement uncertainties, variations in the incoming bar conditions and product changes. The fuzzy C-means algorithm was evaluated for rule-base generation for fuzzy and fuzzy grey-box Temperature estimation. Experimental data were collected from a reallife mill and three different sets were randomly drawn. The first set was used for rule-generation, the second set was used for training those systems with learning capabilities, while the third one was used for validation. The performance of the developed systems was evaluated by five performance measures applied over the prediction error with the validation set and was compared with that of the empirical rule-base fuzzy systems and the physical model used in plant. The results show that the fuzzy C-means generated rule-bases improve Temperature estimation; however, the best results arc obtained when fuzzy C-means algorithm, grey-box modeling and learning functions arc combined. Application of fuzzy C-means rule generation brings improvement on performance of up to 72%.

  • neural fuzzy and grey box modelling for Entry Temperature prediction in a hot strip mill
    Expert Systems With Applications, 2012
    Co-Authors: José Angel Barrios, Miguel Torresalvarado, Alberto Cavazos
    Abstract:

    In hot strip mills, initial controller set points have to be calculated before the steel bar enters the mill. Calculations rely on the good knowledge of rolling variables. Measurements are only available once the bar has entered the mill therefore they have to be estimated. Estimation of process variables, particularly Temperature, is of crucial importance for the bar front section to fulfil quality requirements and it must be performed in the shortest possible time to keep heat. Currently, Temperature estimation is performed by physical modelling, however it is highly affected by measurement uncertainties, variations in the incoming bar conditions and final product changes. In order to overcome these problems, artificial intelligence techniques as artificial neural networks and fuzzy logic have been proposed. In this paper, several neural networks, neural based Grey-Box models, fuzzy inference systems, and fuzzy based Grey-Box models are designed and tested with experimental data to estimate scale breaker Entry Temperature given the relevance of this variable. Their performances are compared against that of the physical model used in plant. Some of the systems presented in this work were proved to have better performance indexes and hence better prediction capabilities than the current physical models used in plant.

  • Neural and Neural Gray-Box Modeling for Entry Temperature Prediction in a Hot Strip Mill
    Journal of Materials Engineering and Performance, 2011
    Co-Authors: José Angel Barrios, Alberto Cavazos, Miguel Torres-alvarado, Luis Leduc
    Abstract:

    In hot strip mills, initial controller set points have to be calculated before the steel bar enters the mill. Calculations rely on the good knowledge of rolling variables. Measurements are available only after the bar has entered the mill, and therefore they have to be estimated. Estimation of process variables, particularly that of Temperature, is of crucial importance for the bar front section to fulfill quality requirements, and the same must be performed in the shortest possible time to preserve heat. Currently, Temperature estimation is performed by physical modeling; however, it is highly affected by measurement uncertainties, variations in the incoming bar conditions, and final product changes. In order to overcome these problems, artificial intelligence techniques such as artificial neural networks and fuzzy logic have been proposed. In this article, neural network-based systems, including neural-based Gray-Box models, are applied to estimate scale breaker Entry Temperature, given its importance, and their performance is compared to that of the physical model used in plant. Several neural systems and several neural-based Gray-Box models are designed and tested with real data. Taking advantage of the flexibility of neural networks for input incorporation, several factors which are believed to have influence on the process are also tested. The systems proposed in this study were proven to have better performance indexes and hence better prediction capabilities than the physical models currently used in plant.

  • fuzzy and fuzzy grey box modelling for Entry Temperature prediction in a hot strip mill
    Materials and Manufacturing Processes, 2011
    Co-Authors: José Angel Barrios, Alberto Cavazos, L A Leduc, Jorge Ramirez
    Abstract:

    In hot strip mills, initial controller set points have to be calculated before the steel bar enters the mill. Calculations rely on the good knowledge of rolling variables. Measurements are only available once the bar has entered the mill; therefore, they have to be estimated. Estimation of process variables, particularly Temperature, is of crucial importance for the bar front section to fulfil quality requirements and must be performed in the shortest possible time to keep heat. Variable estimation is highly affected by measurement uncertainties, variations in the incoming bar conditions, and final product changes. In order to overcome these problems, artificial intelligence techniques, such as fuzzy logic and artificial neural networks, have been proposed. In this article, fuzzy logic-based systems, including fuzzy-based Grey-Box models, are applied to estimate scale breaker Entry Temperature, given its importance, and its performance is compared against that of the physical model used in plant. Six fuzz...

  • Entry Temperature prediction of a hot strip mill by a hybrid learning type 2 fls
    Journal of Intelligent and Fuzzy Systems, 2006
    Co-Authors: Gerardo M Mendez, Alberto Cavazos, Rogelio Soto, L A Leduc
    Abstract:

    In Hot Strip Mills, on-line estimation of rolling variables is of crucial importance in order to Set-Up the Finishing Mill, i.e. setting initial working references for the in-bar regulators, and hence fulfilling quality requirements. This paper presents the experimental results of the application of type-2 fuzzy logic systems for scale breaker Entry Temperature prediction in a real hot strip mill. Since in the literature only back-propagation has been proposed for type-2 fuzzy logic systems, a hybrid learning algorithm has been developed. Such algorithm is also presented. The algorithm uses back-propagation with recursive least-squares and back propagation with square-root filter methods. The systems were tested for three types of inputs: a interval singleton b interval type-1 non-singleton, c interval type-2 non-singleton. The experiments were carried out for three different types of coils. Experimental results show the feasibility of the systems developed here for scale breaker Entry Temperature prediction. Comparison with type-1 fuzzy logic systems shows the hybrid learning type-2 fuzzy logic systems improve performance in scale breaker Entry Temperature prediction under the tested condition.

José Angel Barrios - One of the best experts on this subject based on the ideXlab platform.

  • Fuzzy C-means Rule Generation for Fuzzy Entry Temperature Prediction in a Hot Strip Mill
    Journal of Iron and Steel Research International, 2016
    Co-Authors: José Angel Barrios, César Villanueva, Alberto Cavazos, Rafael Colas
    Abstract:

    Variable estimation for finishing mill set-up in hot rolling is greatly affected by measurement uncertainties, variations in the incoming bar conditions and product changes. The fuzzy C-means algorithm was evaluated for rule-base generation for fuzzy and fuzzy grey-box Temperature estimation. Experimental data were collected from a reallife mill and three different sets were randomly drawn. The first set was used for rule-generation, the second set was used for training those systems with learning capabilities, while the third one was used for validation. The performance of the developed systems was evaluated by five performance measures applied over the prediction error with the validation set and was compared with that of the empirical rule-base fuzzy systems and the physical model used in plant. The results show that the fuzzy C-means generated rule-bases improve Temperature estimation; however, the best results arc obtained when fuzzy C-means algorithm, grey-box modeling and learning functions arc combined. Application of fuzzy C-means rule generation brings improvement on performance of up to 72%.

  • neural fuzzy and grey box modelling for Entry Temperature prediction in a hot strip mill
    Expert Systems With Applications, 2012
    Co-Authors: José Angel Barrios, Miguel Torresalvarado, Alberto Cavazos
    Abstract:

    In hot strip mills, initial controller set points have to be calculated before the steel bar enters the mill. Calculations rely on the good knowledge of rolling variables. Measurements are only available once the bar has entered the mill therefore they have to be estimated. Estimation of process variables, particularly Temperature, is of crucial importance for the bar front section to fulfil quality requirements and it must be performed in the shortest possible time to keep heat. Currently, Temperature estimation is performed by physical modelling, however it is highly affected by measurement uncertainties, variations in the incoming bar conditions and final product changes. In order to overcome these problems, artificial intelligence techniques as artificial neural networks and fuzzy logic have been proposed. In this paper, several neural networks, neural based Grey-Box models, fuzzy inference systems, and fuzzy based Grey-Box models are designed and tested with experimental data to estimate scale breaker Entry Temperature given the relevance of this variable. Their performances are compared against that of the physical model used in plant. Some of the systems presented in this work were proved to have better performance indexes and hence better prediction capabilities than the current physical models used in plant.

  • Neural and Neural Gray-Box Modeling for Entry Temperature Prediction in a Hot Strip Mill
    Journal of Materials Engineering and Performance, 2011
    Co-Authors: José Angel Barrios, Alberto Cavazos, Miguel Torres-alvarado, Luis Leduc
    Abstract:

    In hot strip mills, initial controller set points have to be calculated before the steel bar enters the mill. Calculations rely on the good knowledge of rolling variables. Measurements are available only after the bar has entered the mill, and therefore they have to be estimated. Estimation of process variables, particularly that of Temperature, is of crucial importance for the bar front section to fulfill quality requirements, and the same must be performed in the shortest possible time to preserve heat. Currently, Temperature estimation is performed by physical modeling; however, it is highly affected by measurement uncertainties, variations in the incoming bar conditions, and final product changes. In order to overcome these problems, artificial intelligence techniques such as artificial neural networks and fuzzy logic have been proposed. In this article, neural network-based systems, including neural-based Gray-Box models, are applied to estimate scale breaker Entry Temperature, given its importance, and their performance is compared to that of the physical model used in plant. Several neural systems and several neural-based Gray-Box models are designed and tested with real data. Taking advantage of the flexibility of neural networks for input incorporation, several factors which are believed to have influence on the process are also tested. The systems proposed in this study were proven to have better performance indexes and hence better prediction capabilities than the physical models currently used in plant.

  • fuzzy and fuzzy grey box modelling for Entry Temperature prediction in a hot strip mill
    Materials and Manufacturing Processes, 2011
    Co-Authors: José Angel Barrios, Alberto Cavazos, L A Leduc, Jorge Ramirez
    Abstract:

    In hot strip mills, initial controller set points have to be calculated before the steel bar enters the mill. Calculations rely on the good knowledge of rolling variables. Measurements are only available once the bar has entered the mill; therefore, they have to be estimated. Estimation of process variables, particularly Temperature, is of crucial importance for the bar front section to fulfil quality requirements and must be performed in the shortest possible time to keep heat. Variable estimation is highly affected by measurement uncertainties, variations in the incoming bar conditions, and final product changes. In order to overcome these problems, artificial intelligence techniques, such as fuzzy logic and artificial neural networks, have been proposed. In this article, fuzzy logic-based systems, including fuzzy-based Grey-Box models, are applied to estimate scale breaker Entry Temperature, given its importance, and its performance is compared against that of the physical model used in plant. Six fuzz...

M De Los Angeles Hernandez - One of the best experts on this subject based on the ideXlab platform.

  • hybrid learning for interval type 2 fuzzy logic systems based on orthogonal least squares and back propagation methods
    Information Sciences, 2009
    Co-Authors: Gerardo M Mendez, M De Los Angeles Hernandez
    Abstract:

    This paper presents a novel learning methodology based on a hybrid algorithm for interval type-2 fuzzy logic systems. Since only the back-propagation method has been proposed in the literature for the tuning of both the antecedent and the consequent parameters of type-2 fuzzy logic systems, a hybrid learning algorithm has been developed. The hybrid method uses a recursive orthogonal least-squares method for tuning the consequent parameters and the back-propagation method for tuning the antecedent parameters. Systems were tested for three types of inputs: (a) interval singleton, (b) interval type-1 non-singleton, and (c) interval type-2 non-singleton. Experiments were carried out on the application of hybrid interval type-2 fuzzy logic systems for prediction of the scale breaker Entry Temperature in a real hot strip mill for three different types of coil. The results proved the feasibility of the systems developed here for scale breaker Entry Temperature prediction. Comparison with type-1 fuzzy logic systems shows that hybrid learning interval type-2 fuzzy logic systems provide improved performance under the conditions tested.

Luis Leduc - One of the best experts on this subject based on the ideXlab platform.

  • Neural and Neural Gray-Box Modeling for Entry Temperature Prediction in a Hot Strip Mill
    Journal of Materials Engineering and Performance, 2011
    Co-Authors: José Angel Barrios, Alberto Cavazos, Miguel Torres-alvarado, Luis Leduc
    Abstract:

    In hot strip mills, initial controller set points have to be calculated before the steel bar enters the mill. Calculations rely on the good knowledge of rolling variables. Measurements are available only after the bar has entered the mill, and therefore they have to be estimated. Estimation of process variables, particularly that of Temperature, is of crucial importance for the bar front section to fulfill quality requirements, and the same must be performed in the shortest possible time to preserve heat. Currently, Temperature estimation is performed by physical modeling; however, it is highly affected by measurement uncertainties, variations in the incoming bar conditions, and final product changes. In order to overcome these problems, artificial intelligence techniques such as artificial neural networks and fuzzy logic have been proposed. In this article, neural network-based systems, including neural-based Gray-Box models, are applied to estimate scale breaker Entry Temperature, given its importance, and their performance is compared to that of the physical model used in plant. Several neural systems and several neural-based Gray-Box models are designed and tested with real data. Taking advantage of the flexibility of neural networks for input incorporation, several factors which are believed to have influence on the process are also tested. The systems proposed in this study were proven to have better performance indexes and hence better prediction capabilities than the physical models currently used in plant.