Adaptive Neuro-Fuzzy Inference System for Controlling a Steam Valve

Al-Ridha, Moatasem Yaseen; Al-Nima, Raıd Rafı Omar; Anaz, Ammar Sameer

doi:10.1109/icsengt.2019.8906307

Cited by 7 publications

(1 citation statement)

References 18 publications

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“…It translated real values to fuzzy values according to Mamdani method. The intelligent steam valve (ISV) technology was introduced by Al-Ridha et al [6]. Their approach involved creating a fuzzy logic system that utilized two inputs (pressure and temperature) and one output (valve opening).…”

Section: Introductionmentioning

confidence: 99%

Coronavirus risk factor by Sugeno fuzzy logic

Qasim Hasan,

Omar Al-Nima,

Esmail Mahmmod

2024

IJ-AI

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<span>World recently faced big challenges with the pandemic of coronavirus disease 2019 (COVID-19). Governments suffer from the problem of appropriately identifying the risk factor of this virus and establishing their safety procedures accordingly. This paper concentrates on designing a coronavirus risk factor (CRF) by the power of Sugeno fuzzy logic (SFL). The main advantage of the CRF is that it can provides a quick and suitable risk evaluation. According to the degree of severity, three essential parameters are considered: number of infected cases, number of people in intensive care units (ICU) and number of deaths. All of these parameters are provided per population. Such interesting and promising outcomes are attained, where the total effect is found equal to 95.3%.</span>

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Section: Introductionmentioning

confidence: 99%

Coronavirus risk factor by Sugeno fuzzy logic

Qasim Hasan,

Omar Al-Nima,

Esmail Mahmmod

2024

IJ-AI

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Predicting the discharge coefficient of oblique cylindrical weir using neural network techniques

et al. 2021

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Cylindrical weir shapes offer a steady-state overflow pattern, where the type of weirs can offer a simple design and provide the ease-to-pass floating debris. This study considers a coefficient of discharge (Cd) prediction for oblique cylindrical weir using three diameters, the first is of D1 = 0.11 m, the second is of D2 = 0.09 m, and the third is of D3 = 0.06.5 m, and three inclination angles with respect to channel axis, the first is of θ1 = 90 ͦ, the second is of θ2 = 45 ͦ, and the third is of θ3 = 30 ͦ. The Cd values for total of 56 experiments are estimated by using the radial basis function network (RBFN), in addition of comparing that with the back-propagation neural network (BPNN) and cascade-forward neural network (CFNN). Root mean square error (RMSE), mean square error (MSE), and correlation coefficient (CC) statics are used as metrics measurements. The RBFN attained superior performance comparing to the other neural networks of BPNN and CFNN. It is found that, for the training stage, the RBFN network benchmarked very small RMSE and MSE values of 1.35E-12 and 1.83E-24, respectively and for the testing stage, it also could benchmark very small RMSE and MSE values of 0.0082 and 6.80E-05, respectively.

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