Development of ANFIS models for polycyclic aromatic hydrocarbons (PAHs) formation in sea sediment

Developing an adaptive neuro‐fuzzy inference system based on particle swarm optimization model for forecasting Cr(VI) removal by NiO nanoparticles

“…Statistical parameters 3)-( 6) and reported in Table 3. 13 As can be seen, the low value of statistical parameters indicates the high accuracy of the designed model. Otherwise, a correlation coefficient close to 1 can express the high capability of the model.…”

“…These substances are considered as removal pollutant sorbent because of the large specific area and lack of internal diffusion resistance. 7,8 In recent years, machine-learning approaches such as artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and least square support vector machine have researchers' attention for the prediction of different processes in industries [9][10][11][12][13][14][15] that can be applied to an extra range of systems to predict the behavior of experimental systems. 9,[11][12][13][14] Tahani et al proposed an ANN model to estimate the thermal conductivity of nanofluid.…”

Hybrid intelligence methods for modeling the diffusivity of light hydrocarbons in bitumen

“…This hesitancy on experimental values may occur because of human error or device error [ 47 ]. In data processing, some of these samples reduce the performance of the proposed model called outlier [ 24 ]. In this study, William's plot is applied to the detection of Outliers for the ANFIS-PSO model as the selected model based on the Leverage approach method ( Figure 10 ).…”

“…H presents an (m∗m) matrix, and X shows an (n∗m) matrix where m and n are parameter number and data point number, respectively. H∗ is the critical leverage value, where N is the count of parameters, and P is related to the count of data points [ 24 , 48 ]. Based on William's plot, the detected outlier is eliminated from the data set for each Run.…”

“…Recently, artificial intelligence technologies such as artificial neural network ( ANN ), adaptive neuro-fuzzy inference system ( ANFIS ), and support vector machine ( SVM ) have drawn researchers’ attention because of their high capability, and flexibility in different applications including system classifications, predictive processes, and control systems [ 4 , 20 ] that can be applied to a vast range of systems to predict the behavior of experimental systems [ 21 , 22 , 23 , 24 , 25 ]. To estimate diffusivity, different types of intelligent predictive tools have been reported [ 4 , 26 , 27 , 28 , 29 ].…”