Adaptive Neural Fuzzy Inference System Model Research Articles

AI-assisted prediction of St14 steel sheets formability: Neural-fuzzy systems and crystal plasticity assessments

We've introduced a new technique leveraging artificial intelligence to determine in-plane forming limit strains in St14 sheet metals. A neural-fuzzy AI system was devised to predict safe points on forming limit diagrams, considering aspects like crystal texture, metal sheet thickness, and spherical punch diameter. Neural-fuzzy systems could be utilized in prediction of forming limit curves using linguistic data description. To obtain the data required for training the AI network, in-depth tests, including XRD, metallography, tensile testing, and Nakazima's hemisphere evaluations, were carried out to detail the metallurgical and mechanical properties of St14 sheet metal. Data on texture, the tensile curves, and grain morphology were then incorporated in crystal plasticity models to find hardening constants for St14's single crystals. Using these determined values, Nakazima's tests were replicated through multi-crystal aggregate configurations. We then undertook a comprehensive parameter investigation via 324 crystal plasticity simulations to shed light on the effects of texture, sheet depth, and punch dimensions on St14's formability. This rich dataset paved the way for training an adaptive neural fuzzy inference system (ANFIS), leading to substantial reductions in time and computational needs. The results validate that the ANFIS model offers accurate and reliable predictions, highlighting its viability for wider use in determining forming limit diagrams.

ЭФФЕКТИВНОСТЬ АЛГОРИТМА ДЕРЕВА ПРИНЯТИЯ РЕШЕНИЙ В МОДЕЛИ ANFIS НА ПРИМЕРЕ ВОССТАНОВЛЕНИЯ НЕДОСТАЮЩИХ ДАННЫХ

Missing data in water resources studies prevent planning. For this reason, data estimation studies are carried out. In this study, ANFIS (Adaptive Neural Fuzzy Inference System) was used to complete the missing data. At the study area, the Yesilirmak Basin located in the north of Turkey, input variables from seven stations and output variable from one station were determined. In the research, 80% (378 months of data) of 504 months of the flow data between 1969 and 2011 was used in the training phase and 20% (126 months of data) was employed in the testing one. The decision tree was used instead of the trial and error method in the selection of input variables and determining the number of membership functions in ANFIS models. It was concluded that the ANFIS model established with the information obtained from the decision tree is successful compared to the randomly established ANFIS models. Using the decision tree before ANFIS models are created will not only minimize the time spent on the model development, but also prevent the best of the possible models from being overlooked.

Comparing machine-learning-based black box techniques and white box models to predict rainfall-runoff in a northern area of Iraq, the Little Khabur River.

The rainfall-runoff process is one of the most complex hydrological phenomena. Estimating runoff in the basin is one of the main conditions for planning and optimal use of rainfall. Using machine learning models in various sciences to investigate phenomena for which statistical information is available is a helpful tool. This study investigates and compares the abilities of HEC-HMS and TOPMODEL as white box models and adaptive neural fuzzy inference system (ANFIS) and gene expression programming (GEP) as black box models in rainfall-runoff simulation using 5-year statistical data. Using the inputs of rainfall and temperature of the previous day and discharge in the steps of the previous 2 days reduced the prediction error of both models. Examining the role of different parameters in improving the accuracy of simulations showed that the temperature as an effective parameter in cold months reduces the amount of prediction error. A comparison of R2, RMSE, and MBE showed that black box models are more effective forecasting tools. Among the black box models, the ANFIS model with R2 = 0.82 has performed better than the GEP model with R2 = 0.76. For white box models, the HEC-HMS and TOPMODEL had R2 equal to 0.3 and 0.25, respectively.

Construction of a Chlorophyll Content Prediction Model for Predicting Chlorophyll Content in the Pericarp of Korla Fragrant Pears during the Storage Period

A chlorophyll content prediction model for predicting chlorophyll content in the pericarp of Korla fragrant pears was constructed based on harvest maturity and storage time. This model predicts chlorophyll content in the pericarp of fragrant pears after storage by using the error backpropagation neural network (BPNN), generalized regression neural network (GRNN) and adaptive neural fuzzy inference system (ANFIS). The results demonstrate that chlorophyll content in the pericarp of fragrant pears decreased gradually as the harvest time lengthened. The chlorophyll content in the pericarp of fragrant pears with different maturity levels at harvest decreased continuously with the increase in storage time. According to a comparison of the prediction performances of the BPNN and ANFIS models, it was discovered that the trained GRNN and ANFIS models could predict chlorophyll content in the pericarp of fragrant pears. The ANFIS model showed the best prediction performances when the input membership functions were gasuss2mf (RMSE = 0.006; R2 = 0.993), dsigmf (RMSE = 0.007; R2 = 0.992) and psigmf (RMSE = 0.007; R2 = 0.992). The findings of this study can serve as references for determining the delivery quality and timing of Korla fragrant pears.

Forecasting of groundwater level fluctuations using a hybrid of multi-discrete wavelet transforms with artificial intelligence models

Abstract Groundwater is often one of the significant natural sources of freshwater supply, especially in arid and semi-arid regions, and is of paramount importance. This study provides a new and high accurate technique for forecasting groundwater level (GWL). The artificial intelligence (AI) models include the artificial neural network (ANN) of multi-layer perceptron (MLP) and radial basis function network (RBF), and adaptive neural-fuzzy inference system (ANFIS) models. Input data to the models is the monthly average GWL of 17 piezometers. In this study, a preprocessing of data including the discrete wavelet transform (DWT) and multi-discrete wavelet transform (M-DWT) simultaneously was utilized. The results showed that the hybrid M-DWT-ANN, M-DWT-RBF, and M-DWT-ANFIS models compared to the DWT-ANN, DWT-RBF, and DWT-ANFIS models as well as than regular ANN, RBF, and ANFIS models, had the highest accuracy in forecasting GWL for the 1-, 2-, 3-, and 6-months ahead. Also, the M-DWT-ANN model had the best performance. Overall, the results illustrated that using the M-DWT method as preprocessing of input data can be a valuable tool to increase the predictive model's accuracy and efficiency. The results of this study indicate the potential of M-DWT-AI hybrid models to improve GWL forecasting.

Comparative evaluation of AI ‐based intelligent GEP and ANFIS models in prediction of thermophysical properties of Fe 3 O 4 ‐coated MWCNT hybrid nanofluids for potential application in energy systems

Hybrid nanofluids are gaining popularity owing to the synergistic effects of nanoparticles, which provide them with better heat transfer capabilities than base fluids and normal nanofluids. The thermophysical characteristics of hybrid nanofluids are critical in shaping heat transmission properties. As a result, before using thermophysical qualities in industrial applications, an in-depth investigation of thermophysical properties is required. In this paper, a metamodel framework is constructed to forecast the effect of nanofluid temperature and concentration on numerous thermophysical parameters of Fe3O4-coated MWCNT hybrid nanofluids. Evolutionary gene expression programming (GEP) and an adaptive neural fuzzy inference system (ANFIS) were employed to develop the prediction models. The model was trained using 70% of the datasets, with the remaining 15% used for testing and validation. A variety of statistical measurements and Taylor's diagrams were used to assess the proposed models. The Pearson's correlation coefficient (R), coefficient of determination (R2) was used for the regression index, the error in the model was evaluated with root mean squared error (RMSE). The model's comprehensive assessment additionally includes modern model efficiency indices such as Kling-Gupta efficiency (KGE) and Nash-Sutcliffe efficiency (NSCE). The proposed models demonstrated impressive prediction capabilities. However, the GEP model (R > 0.9825, R2 > 0.9654, RMSE = 0.7929, KGE > 0.9188, and NSCE > 0.9566) outperformed the ANFIS model (R > 0.9601, R2 > 0.9218, RMSE = 1.495, KGE > 0.8015, and NSCE > 0.8745) for the majority of the findings. The generated metamodel was robust enough to replace the repetitive expensive lab procedures required to measure thermophysical properties. Highlights Predictions of thermophysical properties of Fe3O4-coated MWCNT hybrid nanofluid AI-based ANFIS and GEP models performed well on statistical indices ANFIS and GEP-based prognostic models validated and compared with Taylor diagrams

ANFIS with input space division for modeling magnetorheological energy absorber

A magnetorheological energy absorber (MREA) is a promising actuator for vibration and shock mitigation due to its continuous and controllable damping force. To take full advantage of this feature, a high-fidelity model that can accurately predict the MREA dynamic force is required. Therefore, this study focuses on proposing adaptive neural-fuzzy inference system (ANFIS) models for MREA that use the impact speed, displacement, velocity, acceleration and current as inputs and the MREA force as output. Three types of ANFIS models are developed based on different input space division methods, namely, the grid partition (GP) method, subtractive clustering (SC) algorithm and fuzzy c-means (FCM) algorithm. The best ANFIS model is chosen from each category by six criteria, i.e., mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), coefficient of variation (cov), coefficient of determination (R2) and training time. In addition, the desirability function is proposed to further decide the best model because these criteria do not have a uniform trend. Then, a comprehensive comparative analysis of the three best models is performed. The results show that the ANFIS model with 53 FIS rules generated by the FCM algorithm has the largest global desirability of 0.66782, demonstrating that it is the best one to predict the dynamic force of MREA under high-speed impacts. Accordingly, the values of MAE, MAPE, RMSE, cov and R2 of this model varied in ranges of 0.31679-0.57118, 4.34181-8.43942, 0.44808-1.03095, 6.14134-15.23283 and 0.98973-0.99834, respectively, for various data sets.

CANFIS: A Chaos Adaptive Neural Fuzzy Inference System for Workload Prediction in the Cloud

As cloud-based applications become increasingly solicited by companies and individuals, the competition between cloud providers that offer cloud services keeps increasing. To win this competition, cloud providers must provide sufficient computing resources that will satisfy users’ demand for every request. Workload prediction has been investigated extensively to resolve this issue using various techniques. This paper presents a workload prediction method called CANFIS, which combines the Savitzky-Golay (SG) filter and Chaotic time series analysis with the Adaptive Neural Fuzzy Inference System (ANFIS) to make predictions of cloud workloads. The SG filter is used to clean data from noise and outliers, and chaotic analysis is used to investigate the chaotic nature of workload and to build the improved ANFIS model. The proposed method is evaluated using real workload traces from web applications (i.e., Wikipedia and NASA Kennedy traces) and cluster applications (i.e., CPU and Memory of Google cluster). Experimental results show that the proposed CANFIS model can improve prediction accuracy compared to existing techniques, including simple ANFIS, Auto-Regressive Integrated Moving Average (ARIMA), Support Vector Machine (SVM), Long Short Term Memory (LSTM), and Neural Networks based methods. A statistical analysis is also performed using the Friedman test, along with Finner post hoc analysis to verify the efficiency of the proposed prediction model.

Cloud spot price prediction approach using adaptive neural fuzzy inference system with chaos theory

The dynamic pricing of cloud computing is a major challenge for cloud users all over the world. This challenge was first addressed by Amazon under the name of Amazon spot instance market. Cloud users can bid for a spot instance using this market and obtain the requested spot if their bids exceed a dynamically changing spot price. Amazon publicises the spot price but does not reveal how it is determined. In this paper, we perform chaotic time series analysis over the spot price trace. We also develop a chaos based adaptive neural fuzzy inference system (ANFIS) model based on phase-space vectors obtained during the phase of chaotic analysis. Next, we study the effect of chaos existence on the prediction accuracy of the spot price by comparing the proposed chaos-ANFIS model with the baseline ANFIS model (non-chaotic approach). Evaluation results show that the proposed chaos-ANFIS model yields better predictions of spot price compared to the baseline ANFIS model in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE).

Adaptive neuro-fuzzy interference system modelling for chlorpyrifos removal with walnut shell biochar

Accumulation of chlorpyrifos (CP), a pesticide, causes a significant environmental problem in food, surface/ground waters further to human health. The removal of the CP pollutant in surface/wastewater could be achieved by biochar due to the improved physical and chemical properties. In this work, the CP removal capacities of biochar samples derived from walnut shells at various temperatures from 450 to 900 °C were investigated. The experiments were performed as laboratory batch type study and the adsorption efficiency was determined at various conditions such as adsorbent dosage (10–500 mg/L), sorbate concentrations (100–1500 µg/L), contact time (0–300 min), initial pH (3–10), and the number of recycle. By subtracting the pyrolysis temperature from 450 °C to 900 °C, the surface areas were found to increase from 12.9 m 2 /g to 353.3 m 2 /g, respectively. The 143 experimental data were evaluated by a pair of kinetics and isotherm models and the Adaptive Neural Fuzzy Inference System (ANFIS). The developed ANFIS model was 98.56% successful in predicting the CP removal efficiency depending on the adsorption conditions. Walnut Shell Biochar (WSBC) can be applied for CP adsorption with 86.64% removal efficiency under optimum adsorption conditions (adsorbent = 250 µg/L, sorbate = 1000 µg/L, pH = 7.07 and contact time 15 min) thanks to its improved porosity. It was determined that the biochar samples could be reused 5 times. Equilibrium adsorption was observed to conform to the Langmuir isotherm, and the maximum adsorption capacity for WSBC@900 was 3.536 mg/g.

Correction to: Using hybrid artificial intelligence approach based on a neuro‑fuzzy system and evolutionary algorithms for modeling landslide susceptibility in East Azerbaijan Province, Iran

Landslide susceptibility analysis is beneficial information for a wide range of applications, including land use management plans. The present attempt has shed light on an efficient landslide susceptibility mapping framework that involves an adaptive neural-fuzzy inference system (ANFIS), which incorporates three metaheuristic methods including grey wolf optimization (GWO), particle swarm optimization (PSO), and shuffled frog leaping algorithm (SFLA) in the East Azerbaijan of Iran. To achieve this goal, 10 landslide occurrence-related influencing factors were pondered. A sum of 766 locations with landslide potentiality was recognized in the context of the study, and the Pearson correlation technique utilized in order to select the influencing factors in landslide models. The association between landslides and conditioning factors was also evaluated using a probability certainty factor (PCF) model. In the next phase, three data mining techniques were united with the ANFIS model, comprising ANFIS-grey wolf algorithm (ANFIS-GW), ANFIS-particle swarm optimization (ANFIS-PSO), and ANFIS-shuffled frog leaping algorithm (ANFIS-SFLA), were structured by the training dataset. Lastly, the receiver operating characteristic (ROC) and statistical procedures were utilized with the aim of validating and contrasting the predictive capability of the models. The findings of the study in terms of the Pearson correlation technique method for the importance ranking of conditioning factors in the context area uncovered that slope, aspect, normalized difference vegetation index (NDVI), and elevation have the highest impact on the occurrence of the landslide. All in all, the ANFIS-PSO model had high performance on both the training (RMSE = 0.288, MAE = 0.069, AUC = 0.89) and validation dataset (RMSE = 0.309, MAE = 0.097, AUC = 0.89), after which, the ANFIS-GWO model and the ANFIS-SFLA model demonstrated the second and third rates. Besides, the study revealed that benefiting the optimization algorithm with the proper selection of the techniques could facilitate landslide susceptibility modeling.

Performance prediction and upgrading of electroanaerobic baffled reactor using neural-fuzzy method

Considering that awareness of the effects of changes in the important operating variables of a wastewater treatment reactor is essential for its designing and maximizing efficiency, this study is focused on prediction and optimization of the performance of the anaerobic baffled reactor upgraded by an electrolysis process (EABR) using an adaptive neural-fuzzy inference system (ANFIS). A semi-industrial pilot of an anaerobic baffled reactor with a total volume of 72 L was integrated with an electrolysis system, and its operation was monitored for three months, during which 42 datasets were collected. Based on the measurements performed during this time, simulation, prediction, and optimization of the desired output variables, including methane yield, effluent COD, and pH value of the fifth chamber, were done. The input factors of the ANFIS model included the pH value of the second chamber, HRT, current density, influent COD, and voltage. Different ANFIS models were evaluated from the point of view of the cluster radius. The results indicated that the radii of 0.25, 0.15, and 0.85 have the best performance to simulate and predict the methane yield, effluent COD, and pH value of the fifth chamber, respectively, with the prediction, mean absolute percentage error (MAPE) values of 2.1276%, 2.6178%, and 0.2217% and the prediction correlation coefficients of 0.9801, 0.9933, and 0.9699. Finally, using the 10 three-dimensional response surface graphs obtained for each output variable, the optimization of the reactor's important operating variables was conducted to achieve the maximum treatment efficiency. This study showed that the ANFIS model is a proper tool for simulating, predicting, and optimizing EABR and managing the intended operating conditions.

Sound pressure level spectrum analysis by combination of 4D PTV and ANFIS method around automotive side-view mirror models

This paper proposes a data augmentation method based on artificial intelligence (AI) to obtain sound level spectrum as predicting the spatial and temporal data of time-resolved three-dimensional Particle Tracking Velocimetry (4D PTV) data. A 4D PTV has used to measure flow characteristics of three side mirror models adopting the Shake-The-Box (STB) algorithm with four high-speed cameras on a robotic arm for measuring industrial scale. Helium filled soap bubbles are used as tracers in the wind tunnel experiment to characterize flow structures around automobile side mirror models. Full volumetric velocity fields and evolution of vortex structures are obtained and analyzed. Instantaneous pressure fields are deduced by solving a Poisson equation based on the 4D PTV data. To predict spatial and temporal data of velocity field, artificial intelligence (AI)-based data prediction method has applied. Adaptive Neural Fuzzy Inference System (ANFIS) based machine learning algorithm works well to find 4D missing data behind the automobile side mirror model. Using the ANFIS model, power spectrum of velocity fluctuations and sound level spectrum of pressure fluctuations are successfully obtained to assess flow and noise characteristics of three different side mirror models.

Modeling of cutting parameters in turning of PEEK composite using artificial neural networks and adaptive-neural fuzzy inference systems

Polyetheretherketone (PEEK) and its composites are commonly used in the industry. Materials with PEEK are widely used in aeronautical, automotive, mechanical, medical, robotic and biomechanical applications due to superior properties, such as high-temperature work, better chemical resistance, lightweight, good absorbance of energy and high strength. To enhance the tribological and mechanical properties of unreinforced PEEK, short fibers are added to the matrix. In this study, Artificial Neural Networks (ANNs) and the Adaptive-Neural Fuzzy Inference System (ANFIS) are employed to predict the cutting forces during the machining operation of unreinforced and reinforced PEEK with30 v/v% carbon fiber and 30 v/v% glass fiber machining. The cutting speed, feed rate, material type, and cutting tools are defined as input parameters, and the cutting force is defined as the system output. The experimental results and test results that are predicted using the ANN and ANFIS models are compared in terms of the coefficient of determination ( R2) and mean absolute percentage error. The test results reveal that the ANFIS and ANN models provide good prediction accuracy and are convenient for predicting the cutting forces in the turning operation of PEEK.

Forecasting CO pollutant concentration of Tabriz city air using artificial neural network and adaptive neuro-fuzzy inference system and its impact on sustainable development of urban

Tabriz as one of the major industrial cities of Iran is not immune to air pollution and spends many days air-polluted each year. Since one of the goals of sustainable development is to achieve clean and good air for all segments of society and to attract clean air from the citizens of a city. In this study, it was attempted to present an efficient model for predicting CO pollutant concentrations using artificial neural network (ANN) and adaptive neural-fuzzy inference system (ANFIS). Air quality monitoring and developing efficient air pollution models can be suitable in providing requirements for sustainable development goals. For modelling, the meteorological and pollutant data were first obtained from the Meteorological and Environmental Agency of Tabriz city. The model inputs were temperature, wind speed, humidity and contaminant concentration daily hour index and weekly day index. The results of the study at validation step yielded 0.82 and 0.63 in terms of determination coefficient for ANN models and ANFIS models. It was also observed that the Ensemble method worked even better than the single 2 methods.

Application of particle swarm optimization with ANFIS model for double scroll chaotic system

The predictions for the original chaos patterns can be used to correct the distorted chaos pattern which has changed due to any changes whether from undesired disturbance or additional information which can hide under chaos pattern. This information can be recovered when the original chaos pattern is predicted. But unpredictability is most features of chaos, and time series prediction can be used based on the collection of past observations of a variable and analysis it to obtain the underlying relationships and then extrapolate future time series. The additional information often prunes away by several techniques. This paper shows how the chaotic time series prediction is difficult and distort even if Neuro-Fuzzy such as Adaptive Neural Fuzzy Inference System (ANFIS) is used under any disturbance. The paper combined particle swarm (PSO) and (ANFIS) to exam the prediction model and predict the original chaos patterns which comes from the double scroll circuit. Changes in the bias of the nonlinear resistor were used as a disturbance. The predicted chaotic data is compared with data from the chaotic circuit.

DYNAMICAL CONTACT PARAMETER IDENTIFICATION OF SPINDLE-HOLDER-TOOL ASSEMBLIES USING SOFT COMPUTING TECHNIQUES

In industry, the capability to predict the tool point frequency response function (FRF) is an essential matter in order to ensure the stability of cutting processes. Fast and accurate identification of contact parameters in spindle-holder-tool assemblies is very important issue in machining dynamics analysis. This work is an attempt to illustrate the utility of soft computing techniques in identification and prediction contact parameters of spindle-holder-tool assemblies. In this paper, three soft computing techniques, namely, genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO) were used for identification of contact dynamics in spindle-holder-tool assemblies. In order to verify the proposed identification approaches, numerical and experimental analysis of the spindle-holder-tool assembly was carried out and the results are presented. Finally, a model based on the adaptive neural fuzzy inference system (ANFIS) was used to predict the dynamical contact parameters at the holder-tool interface of a spindle-holder-tool assembly. Accuracy and performance of the ANFIS model has been found to be satisfactory while validated with experimental results.

Deep learning techniques for polycaprolactone molecular weight prediction via enzymatic polymerization process

Polycaprolactone (PCL) is a widely used polyester in biomedical field conventionally synthesized under harsh conditions such as high reaction temperature, long reaction time, organic solvents. To address these drawbacks, in this study, PCL is synthesized via enzymatic polymerization using Candida Antarctica lipase B (CALB) as it does not produce toxic residues and operates under mild conditions. Prediction of polymer molecular weight based on the effects of reaction parameters such as temperature and reaction time is critical in understanding the enzymatic polymerization process. Hence in this study feedforward artificial neural network (FANN) and adaptive neural fuzzy inference system (ANFIS) models were used to stimulate the process. Predictions were done with both models by changing training algorithms and sub-parameters in FANN model and number and type of membership functions in ANFIS model. Comparison of results between FANN and ANFIS proved that ANFIS model had better prediction. In ANFIS model, GAUSS GAUSS2 with [8,4] membership function set was the most effective membership functions to develop model for training with low MAE, MAPE, MSE and RMSE values being 0.01872, 0.00012, 0.001226 and 0.035011, respectively. It can achieve with high overall R2 at value of 1 and 99.99% for accuracy of validation.

Efficiency of three advanced data-driven models for predicting axial compression capacity of CFDST columns

Abstract This study aims to investigate the performance of three advanced data-driven models, namely multivariate adaptive regression spline (MARS), artificial neural network (ANN), and adaptive neural fuzzy inference system (ANFIS), for predicting the axial compression capacity of circular concrete-filled double-skin steel tube (CFDST) columns. For this purpose, 125 experimental data sets collected from the literature were used to develop the MARS, ANN, and ANFIS models. In this regard, the column length, the outer diameter, the outer thickness and yield strength of the outer steel tube, the inner diameter, the inner thickness and yield strength of the inner steel tube, and the compressive strength of concrete were considered as input variables, meanwhile, axial compression capacity was considered as the output variable. The performance of the three data-driven models was compared with six equations proposed by design codes and other authors. The comparisons showed that three data-driven models achieved more accuracy than previous equations, of which, the ANN model has an advantage over the ANFIS and MARS models. Finally, a graphical user-friendly interface (GUI) was developed to make the MARS, ANN, and ANFIS models become more attractive for practical use.

Efficient single and dual axis solar tracking system controllers based on adaptive neural fuzzy inference system

Artificial Intelligence is widely used in solar applications. Adaptive Neural Fuzzy Inference System (ANFIS) principle is one of the intelligent techniques that is sufficient to be used in control systems. This paper proposes two new efficient intelligent solar tracking control systems based on ANFIS principle. The aim of this paper is to design and implement efficient single and dual-axis solar tracking control systems that can increase the performance of solar trackers, predict the trajectory of the sun across the sky accurately, and minimize the error, therefore, maximize the energy output of solar tracking systems. Experimental data are used to train and test the proposed solar tracking controllers by using month, day and time as input variables to predict the optimum positions for solar tracking systems (tilt/orientation angles). The proposed ANFIS models have been evaluated to find its capability and robustness in tracking the optimum angles that gain the maximum solar radiation. It is found that the proposed controllers are optimum to control solar tracking systems with high prediction rate and the low error rate. Besides, the selected variables along with the selected architecture could successfully predict the optimum tilt and orientation angles. The proposed models provide superior results with five membership functions, and it could obtain high performance for both single-axis and dual-axis solar tracking systems.