Simulation Results Illustrating the Optimization of Type-2 Fuzzy Controllers

Author(s):  
Oscar Castillo ◽  
Patricia Melin
Author(s):  
Radu-Emil Precup ◽  
Radu-Codrut David ◽  
Raul-Cristian Roman ◽  
Alexandra-Iulia Szedlak-Stinean ◽  
Emil M. Petriu

In recent days, deep learning models become a significant research area because of its applicability in diverse domains. In this paper, we employ an optimal deep neural network (DNN) based model for classifying diabetes disease. The DNN is employed for diagnosing the patient diseases effectively with better performance. To further improve the classifier efficiency, multilayer perceptron (MLP) is employed to remove the misclassified instance in the dataset. Then, the processed data is again provided as input to the DNN based classification model. The use of MLP significantly helps to remove the misclassified instances. The presented optimal data classification model is experimented on the PIMA Indians Diabetes dataset which holds the medical details of 768 patients under the presence of 8 attributes for every record. The obtained simulation results verified the superior nature of the presented model over the compared methods.


2012 ◽  
Vol 192 ◽  
pp. 19-38 ◽  
Author(s):  
Oscar Castillo ◽  
Ricardo Martínez-Marroquín ◽  
Patricia Melin ◽  
Fevrier Valdez ◽  
José Soria

2012 ◽  
Vol 12 (4) ◽  
pp. 1267-1278 ◽  
Author(s):  
Oscar Castillo ◽  
Patricia Melin

2021 ◽  
Vol 54 (4) ◽  
pp. 189-194
Author(s):  
Claudia-Adina Bojan-Dragos ◽  
Radu-Emil Precup ◽  
Stefan Preitl ◽  
Raul-Cristian Roman ◽  
Elena-Lorena Hedrea ◽  
...  

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