Simulation Results of the Type-2 Fuzzy Sugeno Integral

2019 ◽  
pp. 37-51
Author(s):  
Patricia Melin ◽  
Gabriela E. Martinez
Keyword(s):  
Sugeno Integral ◽  
Simulation Results

scholarly journals Optimal Deep Learning based Data Classification Model for Type-2 Diabetes Mellitus Diagnosis and Prediction System

2020 ◽  
Vol 9 (3) ◽  
pp. 1596-1604
Keyword(s):  
Deep Learning ◽  
Data Classification ◽  
Research Area ◽  
Classification Model ◽  
Prediction System ◽  
Pima Indians ◽  
Significant Research ◽  
Diabetes Mellitus Diagnosis ◽  
Simulation Results

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.

scholarly journals Stabilization of inter-area oscillations in two-area test system via centralized interval type-2 fuzzy-based dynamic brake control

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mohamed Fayez ◽  
Mohamed Mandor ◽  
Mohamed El-Hadidy ◽  
Fahmy Bendary
Keyword(s):  
Power Systems ◽  
Case Studies ◽  
Test System ◽  
Interconnected Power Systems ◽  
Power Oscillations ◽  
Simulation Results ◽  
Interval Type ◽  
Degrees Of Severity ◽  
Weak Damping

AbstractInter-area oscillations are, by far, the most detrimental oscillation category to the integrity of synchronously interconnected power systems. Inter-area oscillations are characterized by the inherent weak damping. The inherent poor damping associated with the inter-area oscillations leaves open wide probabilities for irrevocable widespread blackouts with the consequent eventual devastating outcomes measured in terms of the huge economic casualties and the possible human fatalities. The main purpose of this work is to mitigate inter-area power oscillations. This article explores the effectiveness of dual thyristor controlled braking resistor units with Interval Type-2 fuzzy-based centralized architecture for neutralizing the jeopardy of inter-area power oscillations in Kundur’s two-area test system using MATLAB™/Simulink environment. The effectiveness of the proposed scheme is examined by considering four case studies with different degrees of severity. The simulation results show that the proposed scheme is simple yet effective in treating the inter-area oscillations appropriately under the considered case studies.

scholarly journals Similarity, inclusion and entropy measures between type-2 fuzzy sets based on the Sugeno integral

2011 ◽  
Vol 53 (9-10) ◽  
pp. 1788-1797 ◽  
Author(s):  
Chao-Ming Hwang ◽  
Miin-Shen Yang ◽  
Wen-Liang Hung ◽  
E. Stanley Lee
Keyword(s):  
Fuzzy Sets ◽  
Sugeno Integral ◽  
Entropy Measures

scholarly journals Electrical Load Prediction Using Interval Type-2 Atanassov Intuitionist Fuzzy System: Gravitational Search Algorithm Tuning Approach

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3591
Author(s):  
Mojtaba Ahmadieh Khanesar ◽  
Jingyi Lu ◽  
Thomas Smith ◽  
David Branson
Keyword(s):  
State Of The Art ◽  
Renewable Energy Sources ◽  
Superior Performance ◽  
Electrical Load ◽  
Load Prediction ◽  
Prediction Algorithms ◽  
Simulation Results ◽  
Gravitational Search ◽  
Interval Type

Establishing accurate electrical load prediction is vital for pricing and power system management. However, the unpredictable behavior of private and industrial users results in uncertainty in these power systems. Furthermore, the utilization of renewable energy sources, which are often variable in their production rates, also increases the complexity making predictions even more difficult. In this paper an interval type-2 intuitionist fuzzy logic system whose parameters are trained in a hybrid fashion using gravitational search algorithms with the ridge least square algorithm is presented for short-term prediction of electrical loading. Simulation results are provided to compare the performance of the proposed approach with that of state-of-the-art electrical load prediction algorithms for Poland, and five regions of Australia. The simulation results demonstrate the superior performance of the proposed approach over seven different current state-of-the-art prediction algorithms in the literature, namely: SVR, ANN, ELM, EEMD-ELM-GOA, EEMD-ELM-DA, EEMD-ELM-PSO and EEMD-ELM-GWO.

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