Pore diameter of nanoporous anodic alumina: Experimental study and application of ANFIS and MLR

Akbarpour, Hamed; Mohajeri, Mahdi; Akbarpour, Masoumeh

doi:10.1016/j.chemolab.2016.02.012

Cited by 7 publications

(6 citation statements)

References 48 publications

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“…ANFIS is consisted of three key parts including a fuzzy rule base, data and a suitable procedure. The membership functions are defined by data and the inference process would be performed by the procedure (Akbarpour, Mohajeri, & Akbarpour, 2016). Moreover, ANFIS employs a first order Sugeno-type fuzzy inference system, which has two inputs and one output defined as follows:…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

See 1 more Smart Citation

Daily global solar radiation modeling using data-driven techniques and empirical equations in a semi-arid climate

Samadianfard

Majnooni-Heris

Qasem

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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Solar radiation, moisture and temperature are the most vital meteorological variables which affect plant growth. Due to the fact that the global solar radiation (GSR) is scarcely gauged at meteorological stations in developing countries, it is commonly estimated by data-driven techniques or by empirical equations. In this study, support vector regression (SVR), model trees (MT), gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS) and several empirical equations were applied to assess the relations between GSR and several meteorological variables including minimum temperature (T min ), maximum temperature (T max ), relative humidity (RH), sunshine hours (n), maximum sunshine hours (N), corrected clear-sky solar irradiation (ICSKY), day of year (DOY) and extra-terrestrial radiation (R a ). For this purpose, the daily GSR measured from the beginning of 2011 to the end of 2013 at Tabriz synoptic station, which is located in semi-arid regions of Iran, were used. A direct strong relationship was observed to exist between the GSR and n. For evaluating the performances of studied techniques, three different statistical indicators were used namely root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (CC). Additionally, a Taylor diagram was utilized to test the similarity between the observed and predicted GSR values. Results indicated that the SVR-6 with input parameters of R a , RH, T min , T max , n/N had better accuracy in predicting GSR with RMSE of 1.656, MAE of 0.990, CC of 0.980 and WI of 0.990 than the other models. Moreover, MT-6 ranked as the second best model in the prediction of GSR values. As an interesting point, studied empirical equations had lower accuracies comparing with the SVR, GEP, MT and ANFIS methods. For instance, GSR values were computed by Angstrom and Prescott equation, as the best empirical equation, with RMSE of 1.786, MAE of 1.156, CC of 0.977 and WI of 0.988. Conclusively, results from the current study proved that the SVR provided reasonable trends for GSR modeling at Tabriz synoptic station. Furthermore, MT models with linear equations can be implemented with a high degree of simplicity and acceptable precision in GSR estimation.

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Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

“…A typical diagram of ANFIS is presented in Figure 6. For additional information about the details of mentioned layers, readers are referred to Akbarpour et al (2016).…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Daily global solar radiation modeling using data-driven techniques and empirical equations in a semi-arid climate

Samadianfard

Majnooni-Heris

Qasem

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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“…12 [38]. Figure 8 shows the schematic representative structure of the ANFIS model with three inputs and one input [39]. In scope of this study, seven input variables (D, Gs, PI, Sand %, Clay %, e, LI) and one output variable (UCS) are used for all models.…”

Section: Adaptive Neuro-fuzzy Interference Systemsmentioning

confidence: 99%

“…ANFIS is executed with Matlab R2015a software. In ANFIS models, the data used in (11) The schematic representative structure of the ANFIS model [39] real values and normalized values so that it has been tried to see the difference between results and make a conclusion. Table 3 shows detailed parameter of training the FIS.…”

Section: Adaptive Neuro-fuzzy Interference Systemsmentioning

confidence: 99%

The effect of data size of ANFIS and MLR models on prediction of unconfined compression strength of clayey soils

Akan

Keskin

2019

SN Appl. Sci.

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The shear strength (S u) of soils is one of the most widely used parameters for designing structures safely, where Su is found with the unconfined compression test (UCS). Although UCS can be acquired by performing uniaxial compression test it would be extremely helpful to predict the UCS without performing any compression test, namely, using computational methods considering different parameters of soils such as consistency limits, fine grain ratio, liquidity index, and void ratio. The goal of present work is predicting UCS taking into account these soil parameters with the aid of developed Adaptive Neuro-Fuzzy Inference System (ANFIS) model and the Multiple Linear Regression (MLR) analyses. On the other hand, the effect of the size of the training set of designed models on the results is examined, also. For this aim, four different models composed of different training and test set ratios have been constructed and analyzed using ANFIS and MLR. It is concluded that UCS can be predicted using MLR analysis and ANFIS model with best 0.76 and 0.91 values of determination coefficient (R 2) around the x = y line respectively, and the effect of the size of the training set of models on ANFIS is more pronounced than MLR models.

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“…Advanced oxidation processes (AOPs) have attracted great attention for wastewater treatment and air purification in the recent years . These methods provide an advantage of destroying the pollutants, in contrast to conventional techniques such as activated carbon or air stripping which only transfer the contaminants from one phase to another . The mechanism of degradation in AOPs is based on the formation of very reactive radicals, principally hydroxyl radicals (OH•) …”

Section: Introductionmentioning

confidence: 99%

Individual and interaction effects of operating parameters on the photocatalytic degradation under visible light illumination: Response surface methodological approach

et al. 2017

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In this study, for the first time a statistical analysis based on the response surface methodology (RSM) was employed to investigate individual and interaction effects of key operating parameters of the photocatalytic degradation under visible-light irradiation using Ag-S/PEG/TiO 2 . Ag-S/PEG/ TiO 2 is a visible-light-driven photocatalyst and was synthesized (based on earlier research) by co-doping of TiO 2 with silver and sulphur and addition of polyethylene glycol (as a reagent template). In addition, the model pollutant was methylene orange (MO) and the studied operating parameters included the photocatalyst loading, initial concentration of the pollutant, and pH of the solution. The statistics-based experimental design and RSM was utilized to find a quadratic model as a functional relationship between the degradation efficiency and the three operating parameters. The regression analysis with R 2 value of 0.9678 showed a close fit between the model prediction and experimental data of the degradation efficiency. The analysis of variance based on the model indicated that pH of the solution was the most influential factor, while the two other operating parameters were also significant. The efficiency of MO degradation reached 94.0 % under the optimum conditions (i.e. photocatalyst loading of 1.20 g/L, MO concentration of 5 mg/L, and pH of 2).

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Pore diameter of nanoporous anodic alumina: Experimental study and application of ANFIS and MLR

Cited by 7 publications

References 48 publications

Daily global solar radiation modeling using data-driven techniques and empirical equations in a semi-arid climate

Daily global solar radiation modeling using data-driven techniques and empirical equations in a semi-arid climate

The effect of data size of ANFIS and MLR models on prediction of unconfined compression strength of clayey soils

Individual and interaction effects of operating parameters on the photocatalytic degradation under visible light illumination: Response surface methodological approach

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