One‐day ahead wind speed/power prediction based on polynomial autoregressive model

An attenuation relationship model belonging to a region with a high earthquake hazard is important. It is used for engineering studies to know how the peak ground acceleration (PGA) value depends on the distance where there are no stations. This study used earthquakes with magnitudes greater than 4 that IzmirNET recorded between 2009 and 2017 to determine the PGA through an artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), which are widely applied in engineering seismology studies. For this purpose, 2925 records from 62 earthquakes were analysed in the ANN and ANFIS applications. Magnitude, focal depth, hypocentral distance (Rhyp), and site conditions comprise the inputs, and PGA values are the outputs. Using the Karaburun earthquake, we compared the ANN and ANFIS models using different ground motion prediction equations (GMPE) and the appropriate criteria. We determined the proximate values to PGA values measured at IzmirNET stations of the Karaburun earthquake, which was M = 6.2 in 2017, were used to test the ANN and ANFIS. The results were examined and indicated that the ANN and ANFIS are good candidates for obtaining PGA values for future earthquakes in the studied area. In addition, the PGA values of subsequent earthquakes can be calculated more quickly without any preliminary evaluation using an ANN and ANFIS.

Short-term wind speed prediction based on LMD and improved FA optimized combined kernel function LSSVM

Analysis of adsorption isotherms of Ag+, Co+2, and Cu+2 onto zeolites using computational intelligence models

Computational intelligence (CI) tools have been successfully applied in different fields with superior performances. Neuro-fuzzy system (NFS) is one the approach which combines the benefits of two powerful CI tools known as artificial neural networks (ANN) and fuzzy logic. Although NFS has attracted researchers in many areas of study, few of its applications have been undertaken in hydrological modeling. Adaptive Network-based Fuzzy Inference System (ANFIS) is so far the most established NFS technique and this study is an application of ANFIS in river stage prediction by using rainfall and stage antecedents as inputs in the tropical catchment of Bekok River in Malaysia. To evaluate the performance of the ANFIS model, it was compared with a traditional modeling technique known as autoregressive model with exogenous inputs (ARX). The results of this study were evaluated based on several statistical measures such as coefficient of efficiency (CE), coefficient of determination (r2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results showed that ANFIS can successfully predict the river stage and it outperforms ARX model significantly. ANFIS was also found better in estimating peak river stages comparing to ARX model. This study demonstrates the auspicious potential of ANFIS in river stage modeling.

Brushless DC (BLDC) motors are the most popular motors used by the industry because they are easy to control. BLDC motors are generally controlled by artificial controls such as Fuzzy Logic Controller (FLC), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). However, the performance of the BLDC control system in previous studies was compared separately with their respective parameters, making it difficult to evaluate comprehensively. Therefore, in order to investigate the characteristic performance of Fuzzy, ANN, and ANFIS, this article provides a comparison of these artificial controls. Two scenarios of the dynamic tests are conducted to investigate control performance under constant torque-various speed and constant speed-various torque. By dynamic testing, characteristics of Fuzzy, ANN, and ANFIS can be observed as real applications. The testing parameters are: Settling Time, Overshoot and Overdamp (in the graph and average value), and then statistic performance are: Integral Square Error (ISE), Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), and Mean Absolute Error (MAE). The test result in scenario 1 showed that the ANN has a better performance compared to other controllers with the MAE, IAE, ITAE, and ISE value of 31.3003; 105.6280; 208.0630; and 5,7289 e4, respectively. However, in scenario 2, ANN only has a better performance compared to other controllers on just a few parameters. In scenario 2, ANN is indeed able to maintain speed but it has a more ripple value than ANFIS. Even so, the ripple that occurs in ANN does not have too much value compared to the setpoint. Therefore, the MAE value of the ANN is smaller than the ANFIS (18.8937 of ANN and 28.4685 of ANFIS).

Evaluation of ANN and ANFIS modeling ability in the prediction of AISI 1050 steel machining performance

Sensitivity analysis of the system of wind speed distributions

This study used two methodologies to model microwave drying kinetics of tomato slices: artificial neural networks (ANN) and the adaptive neuro-fuzzy inference system (ANFIS).The tomatoes were pre-treated with water blanching (WBP), ascorbic acid (AAP), and sodium metabisulphite (SBP).The tomatoes were then dried in the microwave at 90, 180, and 360 W after being sliced into 4-, 6-, and 8-mm thicknesses.After fitting ANN and ANFIS models to the experimental drying data, the optimal model topology was identified.The predictive accuracy of these models was assessed through these metrics: the coefficient of determination (R 2 ), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE), by contrasting the projected results with experimental data.The results showed a range of 0.92 to 3.75 h for drying time, 0.28 to 2.86 x 10 -8 m 2 /s for Deff, and 0.0027 to 0.0063 kWh/kg for SEC.The results indicated a highperformance capacity of ANFIS compared to ANN, with a higher R 2 of 1.0000 and a lower MSE of 0.9999 to 1.0000, an RMSE of 1.45x10 -11 to 0.00309, and a MAE of 1.15x10 -11 to 0.00296.Consequently, the ANFIS model demonstrated superior predictive capabilities compared to the ANN model, achieving a strong fit with the observed data.

Artificial neural network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) have an extensive range of applications in water resources management. Wavelet transformation as a preprocessing approach can improve the ability of a forecasting model by capturing useful information on various resolution levels. The objective of this research is to compare several data-driven models for forecasting groundwater level for different prediction periods. In this study, a number of model structures for Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Wavelet-ANN and Wavelet-ANFIS models have been compared to evaluate their performances to forecast groundwater level with 1, 2, 3 and 4 months ahead under two case studies in two sub-basins. It was demonstrated that wavelet transform can improve accuracy of groundwater level forecasting. It has been also shown that the forecasts made by Wavelet-ANFIS models are more accurate than those by ANN, ANFIS and Wavelet-ANN models. This study confirms that the optimum number of neurons in the hidden layer cannot be always determined by using a specific formula but trial-and-error method. The decomposition level in wavelet transform should be determined according to the periodicity and seasonality of data series. The prediction of these models is more accurate for 1 and 2 months ahead (for example RMSE = 0.12, E = 0.93 and R 2 = 0.99 for wavelet-ANFIS model for 1 month ahead) than for 3 and 4 months ahead (for example RMSE = 2.07, E = 0.63 and R 2 = 0.91 for wavelet-ANFIS model for 4 months ahead).

Wind power presents undesirable intermittencies due to the considerable variations in the wind speed which may affect adversely the smooth operation of the grid. Effective wind forecast is essential in order to report the amount of energy supply with high accuracy, which is crucial for planning energy resources for power system operators. In this paper a new statistical method is presented based on independent component analysis (ICA) and autoregressive (AR) model. ICA is utilized in order to exploit the hidden factors which may exist in the wind speed time-series. ICA methods based on exploiting the time structure like second order blind identification (SOBI) can be used as a preliminary step in wind speed forecasting.

Due to the increasing need for intelligent systems, the adaptive neuro-fuzzy inference system (ANFIS) has recently attracted the attention of researchers in various scientific and engineering areas. The purpose of this work is to present the modeling of a photovoltaic power supply (PVPS) system using an ANFIS. For the modeling of the PVPS system, it is required to find suitable models for its different components (ANFIS PV-generator, ANFIS battery and ANFIS regulator) under variable climatic conditions. A database of measured weather data (global radiation, temperature and humidity) and electrical signals (photovoltaic, battery and regulator voltage and current) of a PVPS system installed in Tahifet (south of Algeria) has been recorded for the period from 1992 to 1997 using a data acquisition system. These data have been used for the modeling and simulation of the PVPS system. The ANFIS for the PV-generator, battery and regulator have been trained by using 10 signals recorded from the different components of the PVPS system. Each signal is represented by 365*5 values (complete 5-years). A set of data for 4-years have been used for the training of the ANFIS and data for 1-year has been used for the testing of the ANFIS. In this way, the ANFIS was trained to accept and handle a number of unusual cases. The comparison between actual and estimated values obtained from the ANFIS gave satisfactory results. The correlation coefficient between measured values and those estimated by the ANFIS gave good prediction accuracy of 98%. In addition, test results show that the ANFIS performed better than the artificial neural networks (ANN). Predicted electrical signals by the ANFIS can be used for several applications in PV systems.

This paper presents the hysteresis controller, based scheme for distribution static compensator (DSTATCOM) for analysis of faults, line-line fault, line to ground fault, single line to ground fault for power quality improvement. DSTATCOM is used for power quality improvement. It provides the deficient reactive power in the distribution system. Conventionally hysteresis controller uses proportional and integral (PI) controller. In this paper, artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) based DSTATCOM controllers are implemented for the analysis of different faults. This paper also provides the comparison of different faults for ANN and ANFIS based DSTATCOM with the help sequence analyzer. Sequence analyzer is used for the positive, negative and zero sequence analysis of three phase signal. Per unit study of the output voltage is carried out to compare the results of the different faults based on ANN and ANFIS controller. The simulation results show the improved results for the voltage profiles with ANFIS controller. The simulation of the proposed model is done with the help of MATLAB/Simulink software.

Direct measurement of snow water equivalent (SWE) in snow-dominated mountainous areas is difficult, thus its prediction is essential for water resources management in such areas. In addition, because of nonlinear trend of snow spatial distribution and the multiple influencing factors concerning the SWE spatial distribution, statistical models are not usually able to present acceptable results. Therefore, applicable methods that are able to predict nonlinear trends are necessary. In this research, to predict SWE, the Sohrevard Watershed located in northwest of Iran was selected as the case study. Database was collected, and the required maps were derived. Snow depth (SD) at 150 points with two sampling patterns including systematic random sampling and Latin hypercube sampling (LHS), and snow density at 18 points were randomly measured, and then SWE was calculated. SWE was predicted using artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and regression methods. The results showed that the performance of ANN and ANFIS models with two sampling patterns were observed better than the regression method. Moreover, based on most of the efficiency criteria, the efficiency of ANN, ANFIS and regression methods under LHS pattern were observed higher than the systematic random sampling pattern. However, there were no significant differences between the two methods of ANN and ANFIS in SWE prediction. Data of both two sampling patterns had the highest sensitivity to the elevation. In addition, the LHS and the systematic random sampling patterns had the least sensitivity to the profile curvature and plan curvature, respectively.

The applicability of artificial neural networks (ANNs) and the adaptive neuro-fuzzy inference system (ANFIS) for determination of mean velocity and discharge of natural streams is investigated. The 2,184 field data obtained from four different sites on the Sarimsakli and Sosun streams in central Turkey were used in the study. ANNs and ANFIS models use the inputs, water surface velocity and water surface slope, to estimate the mean velocity and discharges of natural streams. The accuracies of both models were compared with the multiple-linear regression (MLR) model. The comparison results showed that the ANFIS model performed better than the ANNs and regression models for estimating mean velocity and discharge. The ANN model also showed better accuracy than the MLR model. The root mean square errors (RMSE) and mean absolute relative errors (MARE) of the MLR model were reduced by 88 and 91 % using the ANFIS model in estimating discharges, respectively. It is found that the optimal ANFIS model with RMSE of 0,063, MARE of 3,47 and determination coefficient (R2) of 0,996 in the test period is superior in estimation of discharge than the MLR model with RMSE of 0,532, MARE of 38,9 and R2 of 0,776, respectively. The study reveals that the ANFIS technique can be successfully used for estimating the mean velocity and discharge of natural streams by using only the inputs of water surface velocity and water surface slope.

Glass fiber reinforced polyester (GFRP) composite materials are widely used in various applications. The prediction of wear values for composite materials is very complex and nonlinear phenomena. Artificial intelligence methods (AI) and expert systems such as artificial neural networks (ANNs) and fuzzy inference systems (FIS) have a series of properties on modeling nonlinear systems. In some situations, ANNs are insufficient under abrupt changes in input variables. Adaptive Neuro Fuzzy Inference System (ANFIS) is capable of integrating the linguistic expressions of FIS with the adaptation and learning skills of the ANNs. The aim of this study is to determine the optimum material content and working conditions in terms of wear resistance. This study proposes an ANFIS sub-clustering based prediction model for estimation of wear behavior of GFRP composites within various concentrations of materials and under diverse loads and speeds. Proposed ANFIS model extracted optimum concentrations and operating parameters to obtain the minimum wear rate. Due to the wear rate estimation model, optimum wear rate value is reached to 25.0013 (mm3/Nm)*10−6 at CaCO3, polystyrene, glass fiber, glass bead, alumina, load and speed values of 49%, 0%, 11%, 10%, 0.8%, 10 N and 100 rpm respectively. A high estimation capability (R2 = 0.964) has been achieved using ANFIS Model.