Fuzzy Neural Classifier Applied to Cases of Synthetic Data
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ABSTRACT This article presents the development of a computational system that allows diffuse neuronal classify cases of synthetic data through patterns overlap with controlled. They built a series of models with neural fuzzy logic and neural networks that were analyzed using different percentages of overlap. Depending on the results obtained, was selected the best model to classify the patterns in accordance with appropriate criteria for performance as permissible and training time. We obtained a model able to identify a type of class, which tends to minimize the errors of classification. The diffuse neuronal model of this type can help specialists from different disciplines to diagnose with a minimum of error, when data are traits with overlapping patterns.
Bishop, C. (1994). Neural Networks and their applications. Rev.Sci.Instrum., 65(6), 1803-1832. DOI: https://doi.org/10.1063/1.1144830
Duch, W.; Adarnzak, R.; Grabczewski, K. (2001). A new methodology of extraction, optimization and application of crisp and fuzzy logical rules. IEEE trans. Neural Networks, 12, 277–306. DOI: https://doi.org/10.1109/72.914524
Duch, W.; Setiono, R.; Zurada, J. (2004). Computacional Methods for Rule-Based Data Understanding. Proceedings of the IEEE, 92(5), 771-805. DOI: https://doi.org/10.1109/JPROC.2004.826605
Hayashi, Y. (1994). Neural expert system using fuzzy teaching input and its application to medical diagnossis, Information scieces applications, 1, 47-58. DOI: https://doi.org/10.1016/1069-0115(94)90019-1
Hayashi, Y.; Imura, A.; Yoshida, K. (1990). Fuzzy neural expert system and its application to medical diagnosis, 8th International Congress on Cybernetics and Systems, New York City, 54-61.
Ma, L.; Chen, H.; Tian, Z.; He, W. (1999). Monitoring the particle size in CFB using fuzzy neural network. United States: American Society of Mechanical Engineers, New York, NY (US).
Mitra, S.; Hayashi, Y. (2000). Neuro-fuzzy rule generation: Survey in soft computing framework. IEEE Transactions on Neural Networks, 11, 748-768. DOI: https://doi.org/10.1109/72.846746
Pal, S.; Mitra, S. (1992). Multilayer perceptron, fuzzy sets, and classification. IEEE Transactions on Neural Networks, 3(5). DOI: https://doi.org/10.1109/72.159058
Pal, S.; Mitra, S. (1999). Neuro-Fuzzy Pattern Methods in soft Computing, Indian Statistical Institute Calcuta. Editorial Wiley and Sons, IN C.
Ramirez, C.; Vladimirova, T.A. (1995). Fuzzy neural network for fuzzy classification En IEEE International Conference on Systems, Man and Cybernetics. Vancouver, Canada, 322-327.
ABSTRACT This article presents the development of a computational system that allows diffuse neuronal classify cases of synthetic data through patterns overlap with controlled. They built a series of models with neural fuzzy logic and neural networks that were analyzed using different percentages of overlap. Depending on the results obtained, was selected the best model to classify the patterns in accordance with appropriate criteria for performance as permissible and training time. We obtained a model able to identify a type of class, which tends to minimize the errors of classification. The diffuse neuronal model of this type can help specialists from different disciplines to diagnose with a minimum of error, when data are traits with overlapping patterns.
Bishop, C. (1994). Neural Networks and their applications. Rev.Sci.Instrum., 65(6), 1803-1832. DOI: https://doi.org/10.1063/1.1144830
Duch, W.; Adarnzak, R.; Grabczewski, K. (2001). A new methodology of extraction, optimization and application of crisp and fuzzy logical rules. IEEE trans. Neural Networks, 12, 277–306. DOI: https://doi.org/10.1109/72.914524
Duch, W.; Setiono, R.; Zurada, J. (2004). Computacional Methods for Rule-Based Data Understanding. Proceedings of the IEEE, 92(5), 771-805. DOI: https://doi.org/10.1109/JPROC.2004.826605
Hayashi, Y. (1994). Neural expert system using fuzzy teaching input and its application to medical diagnossis, Information scieces applications, 1, 47-58. DOI: https://doi.org/10.1016/1069-0115(94)90019-1
Hayashi, Y.; Imura, A.; Yoshida, K. (1990). Fuzzy neural expert system and its application to medical diagnosis, 8th International Congress on Cybernetics and Systems, New York City, 54-61.
Ma, L.; Chen, H.; Tian, Z.; He, W. (1999). Monitoring the particle size in CFB using fuzzy neural network. United States: American Society of Mechanical Engineers, New York, NY (US).
Mitra, S.; Hayashi, Y. (2000). Neuro-fuzzy rule generation: Survey in soft computing framework. IEEE Transactions on Neural Networks, 11, 748-768. DOI: https://doi.org/10.1109/72.846746
Pal, S.; Mitra, S. (1992). Multilayer perceptron, fuzzy sets, and classification. IEEE Transactions on Neural Networks, 3(5). DOI: https://doi.org/10.1109/72.159058
Pal, S.; Mitra, S. (1999). Neuro-Fuzzy Pattern Methods in soft Computing, Indian Statistical Institute Calcuta. Editorial Wiley and Sons, IN C.
Ramirez, C.; Vladimirova, T.A. (1995). Fuzzy neural network for fuzzy classification En IEEE International Conference on Systems, Man and Cybernetics. Vancouver, Canada, 322-327.
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