Performance Of Adaptive Neuro-fuzzy Inference System Research Articles

Enhancing Intrusion Detection Systems with Adaptive Neuro-Fuzzy Inference Systems

Network security has become increasingly critical in recent years. Among the various aspects of network security and considering several approaches to network security, intrusion detection systems (IDSs) have gained considerable attention. The prominence of this factor, among other factors of network security, is due to its ability to address the complex and uncertain nature of security breaches. Whenever data flow over the network, precise categorization of normal and malicious data is necessary. Past IDS systems lack precise categorization. Thus, the present study focuses on the use of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier to categorize network instances into malicious types and normal behavior. Using the KDD99 dataset, the performance of ANFIS is evaluated and compared with that of traditional machine learning models such as decision trees and multilayer perceptrons. Through experimentation with different membership functions, such as Gaussian, triangular, bell-shaped, and sigmoidal functions, Gaussian functions are identified as optimal for this specific task. The results underscore the effectiveness of ANFIS, leveraging the strengths of both artificial neural networks (ANNs) and fuzzy reasoning systems. ANFIS demonstrates superior capabilities in understanding nonlinear interaction patterns, adapting to evolving threats, and facilitating rapid learning in intrusion detection applications.

Efficiency increment of CFD modeling by using ANFIS artificial intelligence for thermal-based separation modeling

This study presents a comprehensive analysis of predicting the temperature in a vacuum membrane distillation (VMD) process. The simulation was carried out via computational fluid dynamics (CFD) as well as machine learning. CFD was performed for obtaining temperature distribution in the feed solution, and the calculated temperature was used for training several machine learning models. The dataset comprises over 13,000 observations, which provide a rich source for modeling complex relationships. Three sophisticated regression models were employed: Adaptive Neuro-Fuzzy Inference System (ANFIS), Kernel Ridge Regression (KRR), and Multi-Layer Perceptron (MLP). ANFIS was chosen for its hybrid nature, combining neural networks and fuzzy logic, effectively capturing intricate non-linear relationships in data. ANFIS performance in fitting the data was compared with the other models. Hyper-parameter optimization for these models was conducted using the Tabu Search algorithm to ensure optimal performance. The ANFIS model demonstrated superior performance with an R2 score of 0.9964513 on the training set and 0.9964507 on the test set, alongside a MSE of 0.037655 and a MAE of 0.168272. The robustness of ANFIS was further confirmed by a 3-fold cross-validation mean R2 score of 0.9964579 and a standard deviation of 3.3619616e−05.

Notice of Violation of IEEE Publication Principles: A Hybrid ANFIS/ABC-based Online Selective Harmonic Elimination Switching Pattern for Cascaded Multi-level Inverters of Microgrids

Notice of Violation of IEEE Publication Principles A Hybrid ANFIS/ABC-based Online Selective Harmonic Elimination Switching Pattern for Cascaded Multi-level Inverters of Microgrids, by H. R. Baghaee; M. Mirsalim; G. B. Gharehpetian; H. A. Talebi; A. Niknam-Kumle, in the IEEE Transactions on Industrial Electronics After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. This authorship of this paper was revised without providing attribution to, or permission from, the coauthors of an earlier version of the manuscript. Alireza Niknam-Kumle was responsible for removing the original coauthor names and replacing them with new coauthors, and then resubmitting the content in a new manuscript to IEEE Transactions on Industrial Electronics Due to the nature of this violation, reasonable effort should be made to remove all past and future references to this paper. Control of electronically-coupled distributed energy resources and topology/switching strategy of power converters have the essential roles to provide high quality power for microgrids including unbalanced and nonlinear loads. Multi-level inverters (MLIs) are used to act as high quality converters with more conversion efficiency, and less switching-frequency/switching-losses. Nevertheless their nonlinearity, complexity, and solvability features of selective harmonic elimination strategy that has affected its industrial applications, it can provide desirable output waveform by keeping fundamental component in its desired value as well as eliminating/minimizing of low-order harmonics. This paper presents adaptive neuro-fuzzy inference system (ANFIS) as a switching angle estimator for harmonic optimization problem in cascade MLIs, which is trained based on the database including optimal switching angles obtained for inverter with non-equal DC sources by artificial bee colony (ABC) algorithm. The reliable search ability, accuracy and convergence time of ABC have been compared with particle swarm optimization algorithm. Also, performance of ANFIS is compared with artificial neural network based on correlation coefficient and root mean square error. Simulation results, experimental tests and real-time simulations reveals the effectiveness of proposed estimator for online elimination of low-order harmonics of MLIs in microgrids and its superiority over other methods.

Performance analysis of adaptive neuro-fuzzy inference-based relying on modeling electric modulus feature of 3-formylchromone

The performance of ANFIS (Adaptive Neuro-Fuzzy Inference System) for modelling and foretell 3-formylchromone's electric modulus conduct was explored. Based on experimental data of the M/, M//; real and imaginary components of the M*, complex electric modulus in temperatures portions (298–388 K), two mathematical models (empirical expressions) were developed. The data divided into training and testing two groups by ratio 80% and 20% of dataset, respectively.In ANFIS, neural networks are combined with fuzzy logic to advance prediction ability. A hybrid learning technique was employed to construct ANFIS models. The training as well as testing findings was in accordance with the experimental results. The outcomes revealed that the correlation determination coefficient was the highest and the root mean square error was the lowest. The findings support the ANFIS model's capacity to simulate and anticipate the electric modulus conduct in 3-formylchromone.

Evaluating Latency in Fifth-Generation Vehicle-to-Everything Communications using Adaptive Neuro-Fuzzy Inference System Model

The fifth-generation new radio technology (5G NR) introduces improved functions to the air interface. In addition, the 5G NR non-standalone (NSA) will operate with long-term evolution, enabling vehicle-to-everything communications (V2X) for improved infotainment services. V2X includes four main classes of communications: vehicle-to-vehicle, vehicle-to-infrastructure, vehicle-to-pedestrian devices, and vehicle-to-network. However, the stringent transmission frequency, latency, and throughput requirements of infotainment applications constrain the transmitting packets of 5G-V2X-based NSA in highway scenarios. In this paper, the latency is improved by preventing the physical layer of gNodeB and the user equipment (UE) from sending redundant packets for service in a highway scenario. The proposed approach adopts an adaptive neuro-fuzzy inference system (ANFIS), a powerful modeling technique based on artificial neural networks ,and a fuzzy inference system. The performance of ANFIS is compared with that of the traditional 5G V2X NSA architecture in a simulation study using Voice over Internet Protocol (VoIP) traffic. The delays, throughputs, and packet losses of both architectures are determined in radio link control (RLC) and VoIP applications. The switch-modes, signal-to-interference-noise ratios (SINRs), hybrid automatic repeat request (HARQ) error rate, channel quality indicators (CQIs), served blocks, and transmission-state of gNodeB are computed for the two architectures for device-to-device (D2D), uplink (UL) and downlink (DL) traffic directions. The simulation results show comparable SINRs, CQIs, served blocks ,andswitchmodes in both scenarios, but the presented ANFIS model significantly outperforms the traditional architecture in delay by 66% in D2D, 29% in UL, 25% in DL, and packet loss by 21% in UL in RLC, the HARQ error rate by 9% in D2D, 30% in UL, 95% in DL, transmission-state in gNodeB by 29%, and the delay by 4% for UEs, and frame loss by 90%for UE in VoIP.

Adaptive neuro-fuzzy inference system based data interpolation for particle image velocimetry in fluid flow applications

This paper presents an adaptive neuro-fuzzy inference system (ANFIS) approach for recovering the missing velocity vectors that commonly occur during fluid flow measurements in fluid mechanics. The capability of ANFIS in refilling the missing data is demonstrated with two case studies. First, the ANFIS is applied to estimate the velocity field data within the masked region of an available particle image velocimetry (PIV) experiment. Then, the ANFIS is trained using the data from outside the masked region and learns the relationship between the velocity vectors. Thus, it predicts the fluid patterns within the gappy area based on its understanding. ANFIS is also applied in another study to capture the small feature in the fluid flow. The vortices within a small area behind an obstacle are removed, and the rest of the data is introduced into the ANFIS model. The efficacy of the proposed ANFIS algorithm in predicting the missing velocity vectors is compared to both artificial neural network (ANN) and 2D cubic interpolation algorithms. It is found that intelligent algorithms (ANFIS and ANN) can predict the presence of vortices in the fluid flow even when there is no information about a circulating flow in the training dataset. The performance of ANFIS (R2= 0.91) in accurately predicting the velocity vectors is superior to the conventional ANN (R2= 0.86).

Adaptive-neuro-fuzzy-inference-system model for prediction of ultimate load of rectangular concrete-filled steel tubular columns

This study is devoted to the development of an Adaptive-Neuro-Fuzzy-Inference-System (ANFIS) model for the prediction of ultimate load of rectangular concrete-filled steel tubular structural members. The learning process of the model is performed by conducting a combination of backpropagation gradient descent and least-squares techniques. The performance of the model is examined by several quality metrics such as coefficient of determination (R2), Root-Mean-Squared-Error (RMSE), Mean-Absolute-Error (MAE), Index of Agreement (IA) and Slope of linear regression. Monte Carlo random sampling technique is employed to propagate input variations to the output response. Moreover, the performance of ANFIS is also compared with other machine learning models including Artificial Neural Network (ANN), Support Vector Machine (SVM), Gaussian Process Regression (GPR) and Ensemble. Results show that the ANFIS model yields higher prediction performance than other machine learning models, for both training and testing data points and regarding all quality metrics. For instance, using training data points, the ANFIS model exhibits a RMSE of 0.0283 compared to 0.0342, 0.0588, 0.0291, and 0.0464 using ANN, Ensemble, GPR, and SVM, respectively (the corresponding gain values are+17.3%,+51.9%,+2.8%, and+39.0%, respectively). On the other hand, using testing data points, the ANFIS model exhibits a RMSE of 0.0276 compared to 0.0393, 0.0987, 0.0403, and 0.0460 using ANN, Ensemble, GPR, and SVM, respectively (the corresponding values of gain are+29.8%,+72.1%,+31.5%, and+40.0%, respectively). The same observation can be made for other quality metrics. It can be concluded that the ANFIS model outperforms other models for both training and testing datasets. The ANFIS model is also compared with existing works in the past, showing its improvement in prediction results. Finally, sensitivity analysis is performed to determine the degree of effect of the input parameters on the ultimate load.

Performance evaluation of ANFIS and RSM modeling in predicting biogas and methane yields from Arachis hypogea shells pretreated with size reduction

In this study, Response Surface Methodology (RSM) was used to examine the effects of temperature, hydraulic retention time, and particle size of Arachis hypogea shell on biogas and methane yields in a batch test. Further to this, an Adaptive Neuro-fuzzy Inference System (ANFIS) clustered with fuzzy c-means (FCM) was developed to predict organic dry matter biogas yield (oDMBY), fresh mass biogas yield (FMBY), organic dry matter methane yield (oDMMY), and fresh mass methane yield (FMMY). Relevant statistical metrics like root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute deviation (MAD), and correlation coefficient (R2) were used to evaluate the performance of the developed ANFIS model. The performance of both RSM and ANFIS were compared based on the performance metrics. The R2 values of RSM for oDMBY, FMBY, oDMMY and FMMY are 0.6268, 0.5875, 0.6109 and 0.5547 respectively; and 0.9601, 0.9486, 0.9626 and 0.9172 respectively for ANFIS model. The results revealed the better performance of the ANFIS than the RSM, with lesser prediction error and higher accuracy. It is concluded that RSM and ANFIS are practical models for predicting particle size limits in a multiple-input parameter without attempting any experiment within a short period with a tiny error rate.

Predicting of acid red 14 removals from synthetic wastewater in the advanced oxidation process using artificial neural networks and fuzzy regression

The titanium dioxide (TiO2) photocatalyst reactor is a widely used approach for eliminating non-biodegradable compounds in water and wastewater. Predicting the performance of the reactor in removing acid red 14 (AR14) was investigated using radial basis function (RBF), Adaptive Neuro-Fuzzy Inference System (ANFIS), and fuzzy regression analysis. The input variables in the models were radiation, irradiation time, the initial concentration of nanoparticle TiO2 to cement ratio (nTiO2/c), pH, initial concentration of AR14, flow rate, and initial concentration of oxidizing peroxydisulfate. In addition, for reducing the overfitting of the training process, we used K-fold cross-validation. The results of using the three methods indicated that the ANFIS performance was more suitable in comparison with the fuzzy regression models, such that the coefficient of determination (R2), index of agreement (IA), Nash–Sutcliffe efficiency (E) for model efficiency, Mean Squared Error (MSE), and Mean Bias Error (MBE) for training between the observed data and predicted data reached 0.965, 0.991, 0.73, 0.0132 and 0.019, respectively. Normalized input data improved slightly the training processes and performance of RBF compared with non-normalized input data. The ANFIS neural network for training process and model performance slightly achieved better results than the RBF neural network. The performance of the fuzzy regression approach was less suitable in comparison with RBF and ANFIS modeling. The sensitivity analysis of the RBF neural network depicted that the initial concentration of peroxydisulfate (S2O8) was the most effective factor (25.6%) in predicting the removal of AR14 from synthetic wastewater. After that, the effective factors were irradiation time (12.44%), initial concentration of AR14 (10.37%), pH (10.37), flow rate (8.55%), and the weight ratio of the initial concentration of nanoparticle TiO2 to cement ratio (nTiO2/c) (1.75%), respectively.

Modeling passengers’ perceptions of intercity train service quality for regular and special days

The Adaptive Neuro-Fuzzy Inference System (ANFIS) is used in this study to model the non-linear relationship between intercity train service quality (SQ) and its attributes related to physical conditions and service features. We use Likert scale questionnaire survey data from 1037 and 553 users to calibrate the ANFIS structures for intercity train SQ estimation for regular days and special days, respectively. The influences of membership functions (MFs) and epochs on ANFIS performance are assessed to capture heterogeneity in the collected SQ data. Based on this study, it is found that the effect of epochs is insignificant for a higher number of epochs. Moreover, the Gaussian-type MF incorporated into the ANFIS structure fits the collected survey data better than other distributions. Overall, the proposed ANFIS structures with 18 attributes show 54.1% and 60.2% accuracy in predicting train SQ for regular days and special days, respectively. A stepwise approach is followed for ranking the intercity train SQ attributes influencing its overall SQ and the results are compared with those of the empirical observations (public opinions). The study implies that besides waiting place condition, attributes related to physical conditions and service features of intercity train are important determinants of its perceived SQ for regular days and special days, respectively. These results help in identifying the characteristics that are important to SQ perception. This can help transit planners and managers in targeting improvement investments that will be most effective to help commuters think more positively about their trips.

Intelligent Prediction of Blasting-Induced Ground Vibration Using ANFIS Optimized by GA and PSO

Ground vibration induced by rock blasting is one of the most crucial problems in surface mines and tunneling projects. Hence, accurate prediction of ground vibration is an important prerequisite in the minimization of its environmental impacts. This study proposes hybrid intelligent models to predict ground vibration using adaptive neuro-fuzzy inference system (ANFIS) optimized by particle swarm optimization (PSO) and genetic algorithms (GAs). To build prediction models using ANFIS, ANFIS–GA, and ANFIS–PSO, a database was established, consisting of 86 data samples gathered from two quarries in Iran. The input parameters of the proposed models were the burden, spacing, stemming, powder factor, maximum charge per delay (MCD), and distance from the blast points, while peak particle velocity (PPV) was considered as the output parameter. Based on the sensitivity analysis results, MCD was found as the most effective parameter of PPV. To check the applicability and efficiency of the proposed models, several traditional performance indices such as determination coefficient (R2) and root-mean-square error (RMSE) were computed. The obtained results showed that the proposed ANFIS–GA and ANFIS–PSO models were capable of statistically predicting ground vibration with excellent levels of accuracy. Compared to the ANFIS, the ANFIS–GA model showed an approximately 61% decrease in RMSE and 10% increase in R2. Also, the ANFIS–PSO model showed an approximately 53% decrease in RMSE and 9% increase in R2 compared to ANFIS. In other words, the ANFIS performance was optimized with the use of GA and PSO.

Adaptive Neuro Fuzzy Inference System (ANFIS) approach for modeling paddy production data in Central Java

The aim of this research is to develop the procedure of constructing an adaptive neuro-fuzzy inference system (ANFIS) model for time series data. The procedure of development applies statistical inference for optimizing ANFIS architecture. In this study, the procedure of Lagrange multiplier (LM) test is used for selecting input variables. Firstly, several lags which are indicated significantly different to zero are divided into 2 clusters, and these lags are selected as optimal inputs of ANFIS based on LM test. Secondly, the cluster numbers of inputs are also determined by using LM-test procedure. Based on this result, a number of rule-bases are generated. The developed model is applied for forecasting paddy production data in Central Java. This study concluded that lag-1, lag-2 and lag-5 with 2 clusters are selected as the optimal inputs. The 1-1 and 2-2 rules are selected as optimal rules. Finally, the model can work well, and generates a very satisfying result in forecasting paddy production data. Based on the root mean squares error (RMSE) and mean absolute percentage error (MAPE) values, the ANFIS performance is better than performance of Autoregressive Integrated Moving Average (ARIMA) for forecasting.

Stress wave evaluation for predicting the properties of thermally modified wood using neuro-fuzzy and neural network modeling

AbstractThis study investigated using the stress wave method to predict the properties of thermally modified wood by means of an adaptive neuro-fuzzy inference system (ANFIS) and neural network (NN) modeling. The stress wave was detected using a pair of accelerometers and an acoustic emission (AE) sensor, and the effect of heat treatment (HT) on the physical and mechanical properties of wood as well as wave velocity and AE signal is discussed. The AE signal was processed in the time and time-frequency domains using wavelet analysis and different features were extracted for network training. The auto-associative NN is used as a dimensional reduction method to decrease the dimension of the extracted AE features and enhance the ANFIS performance. It was shown that while the stress wave velocity using the accelerometer did not result in an accurate model, the network performance significantly increased when trained with the AE features. The AE signal exhibited a significant correlation with wood treatment and porosity. The best ANFIS performance corresponded to predicting the wood swelling coefficient, equilibrium moisture content (EMC) and water absorption (WA), respectively. However, the AE signal did not seem suitable for predicting the wood density and hardness. The performance of ANFIS was compared with the “group method of data handling” (GMDH) NN. Both the ANFIS and GMDH networks showed higher accuracy than the multivariate linear regression (MVLR) model.

Comparative evaluation of intelligent algorithms to improve adaptive neuro-fuzzy inference system performance in precipitation modelling

Existent algorithms to train adaptive neuro-fuzzy inference system (ANFIS) such as gradient descent and least-square methods are useful, but they have problems such as trapping in local optimum as well as high-volume of computations. This paper examines the application of genetic algorithm (GA), ant colony optimization for the continuous domain (ACOR), particle swarm optimization (PSO) and differential evolution (DE) for improving the performance of ANFIS models in simulating monthly rainfall magnitudes. Monthly rainfall records of nine foothills to arid weather stations of Isfahan province, central Iran were used for evaluating the proposed methodology. The correlation coefficient and non-linear sensitivity analysis were performed on the available rainfall records. Monthly rainfall modelling was carried out based on three input groups and used separately in five applied models (e.g. ANFIS, ANFIS-PSO, ANFIS-ACOR, ANFIS-DE, and ANFIS-GA). The obtained results showed that proposed hybrid models had better accuracy than the simple ANFIS model in escaping local optima. The best model was ANFIS-ACOR (with R2 = 0.92, RMSE = 2.73 mm and SI = 0.26), while sole ANFIS model (with R2 = 0.33, RMSE = 9.98 mm and SI = 0.90) presented the worst output. Moreover, the obtained results showed that: (1) Coupling the optimization algorithms with ANFIS can improve its performance in modeling the monthly rainfall time series; (2) the correlation coefficient and sensitivity analysis of input data provided a valid input selection that could improve the model accuracy with neglecting the redundant input parameters.

Discussion of ‘Modeling water table depth using adaptive Neuro-Fuzzy Inference System by Umesh Kumar Das, Parthajit Roy and Dillip Kumar Ghose (2017)

ABSTRACT This paper is about the discussion of ‘Modeling water table depth using adaptive Neuro-Fuzzy Inference System by Umesh Kumar Das, Parthajit Roy and Dillip Kumar Ghose (ISH Journal of Hydraulic Engineering, 29 December 2017, DOI: 10.1080/09715010.2017.1420497). The authors of the original paper presented the application of Back Propagation Neural Network (BPNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models to predict the water table depth of an aquifer by considering the precipitation, temperature, humidity, surface runoff and evapotranspiration loss as the input of the models. Five different input combinations were examined in the modeling. The authors concluded that the performance of ANFIS is better as compared to BPNN by considering several evaluation criteria. The discusser appreciates this type of study, and the results could be extremely useful for engineers or other audiences. Although the methodology and findings observed in the study are reasonable, this discussion calls attention to add a few issues that may require further clarification.

Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability.

In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations.

DCE-MRI prediction of survival time for patients with glioblastoma multiforme: using an adaptive neuro-fuzzy-based model and nested model selection technique.

This pilot study investigates the construction of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the prediction of the survival time of patients with glioblastoma multiforme (GBM). ANFIS is trained by the pharmacokinetic (PK) parameters estimated by the model selection (MS) technique in dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) data analysis, and patient age. DCE-MRI investigations of 33 treatment-naïve patients with GBM were studied. Using the modified Tofts model and MS technique, the following physiologically nested models were constructed: Model 1, no vascular leakage (normal tissue); Model 2, leakage without efflux; Model 3, leakage with bidirectional exchange (influx and efflux). For each patient, the PK parameters of the three models were estimated as follows: blood plasma volume (vp ) for Model 1; vp and volume transfer constant (Ktrans ) for Model 2; vp , Ktrans and rate constant (kep ) for Model 3. Using Cox regression analysis, the best combination of the estimated PK parameters, together with patient age, was identified for the design and training of ANFIS. A K-fold cross-validation (K=33) technique was employed for training, testing and optimization of ANFIS. Given the survival time distribution, three classes of survival were determined and a confusion matrix for the correct classification fraction (CCF) of the trained ANFIS was estimated as an accuracy index of ANFIS's performance. Patient age, kep and ve (Ktrans /kep ) of Model 3, and Ktrans of Model 2, were found to be the most effective parameters for training ANFIS. The CCF of the trained ANFIS was 84.8%. High diagonal elements of the confusion matrix (81.8%, 90.1% and 81.8% for Class 1, Class 2 and Class 3, respectively), with low off-diagonal elements, strongly confirmed the robustness and high performance of the trained ANFIS for predicting the three survival classes. This study confirms that DCE-MRI PK analysis, combined with the MS technique and ANFIS, allows the construction of a DCE-MRI-based fuzzy integrated predictor for the prediction of the survival of patients with GBM.

Comparative Prediction of Groundwater Fluctuation by CWTFT-ANFIS and WT-ANFIS

Objective: To evaluate the ability of prediction performance of ANFIS (Adaptive Neuro Fuzzy Inference System) model on groundwater level fluctuation in Lower Bhavani River Basin (LBRB). Method: To improve the accuracy of prediction of ANFIS model, WT (Wavelet Transform) and CWTFT (Continuous Wavelet Fast Fourier Transform) are performed as preprocessing techniques. Model development is carried out through three different input parameters (duration, groundwater recharge and groundwater discharge) and one output parameter (groundwater fluctuation) for the period of 2009-2015 on monthly stress basis. Findings: Based on the comparative prediction by the models CWTFT-ANFIS and WTANFIS over conventional ANFIS model through calibration and validation process on different reaches of the study area, optimum model is identified. Statistical indices are performed to measure the best fitting and trend identification for the each prediction.

Studi Implementasi Adaptive Neuro Fuzzy Inference System Untuk Menentukan Normalitas Kehamilan

Early detection of normality pregnancy is one of the ways to prevent more serious disorders in pregnancy. This thesis study the implementation of Adaptive Neuro Fuzzy Inference System (ANFIS) to determine the normality of pregnancy. The period of pregnancy and complaints during pregnancy are used as inputs and the normality of pregnancy as output. Data were analyzed using ANFIS method and using Sugeno FIS rules. The program simulation results show that the performance of ANFIS can be implemented to determine the normality of pregnancy. The learning results on different training with the highest level of accuracy of 77,5% can recognize the symptoms and 97.5% could identify the diagnosis to determine the normality of pregnancy. The system can provide the necessary information about the normality of pregnancy. The results show that ANFIS can be used to determine the normality of pregnancy.

Anfis model for vibration signals based on aging process in electric motors

In this study, the aging process of an electric motor is accomplished by adaptive neuro-fuzzy inference system (ANFIS) using vibration signals. Different ANFIS models are compared for representing the aging process in the best possible way. An artificial aging experiment is performed and vibration data taken from the initial (healthy) and final (faulty) cases are used to identify the aging process. Four different ANFIS models are presented. Moving average (MA) filters are applied to the input and output pairs for different lagging factors to change the smoothness degree of the data and thus the performance of system identification. The success of the models is evaluated on three conditions; the performance of the ANFIS and the linear correlation between expected output (faulty case data) and aging model output, in time and frequency domains.The study also evaluates the influence of preprocessing using MA filtering on the ANFIS performance for vibration data which have stochastic characteristics.