Adaptive Network-based Fuzzy Inference Research Articles

An Adaptive Network-Based Approach for Advanced Forecasting of Cryptocurrency Values

This paper describes an architecture for predicting the price of cryptocurrencies for the next seven days using the Adaptive Network Based Fuzzy Inference System (ANFIS). Historical data of cryptocurrencies and indexes that are considered are Bitcoin (BTC), Ethereum (ETH), Bitcoin Dominance (BTC.D), and Ethereum Dominance (ETH.D) in adaily timeframe. The methods used to teach the data are hybrid and backpropagation algorithms, as well as grid partition, subtractive clustering, and Fuzzy C-means clustering (FCM) algorithms, which are used in data clustering. The architectural performance designed in this paper has been compared with different inputs and neural network models in terms of statistical evaluation criteria. Finally, the proposed method can predict the price of digital currencies in a short time.

Water level prediction using soft computing techniques: A case study in the Malwathu Oya, Sri Lanka.

Hydrologic models to simulate river flows are computationally costly. In addition to the precipitation and other meteorological time series, catchment characteristics, including soil data, land use, land cover, and roughness, are essential in most hydrologic models. The unavailability of these data series challenged the accuracy of simulations. However, recent advances in soft computing techniques offer better approaches and solutions at less computational complexity. These require a minimum amount of data, while they reach higher accuracies depending on the quality of data sets. The Gradient Boosting Algorithms and Adaptive Network-based Fuzzy Inference System (ANFIS) are two such systems that can be used in simulating river flows based on the catchment rainfall. In this paper, the computational capabilities of these two systems were tested in simulated river flows by developing the prediction models for Malwathu Oya in Sri Lanka. The simulated flows were then compared with the ground-measured river flows for accuracy. Correlation of coefficient (R), Per cent-Bias (bias), Nash Sutcliffe Model efficiency (NSE), Mean Absolute Relative Error (MARE), Kling-Gupta Efficiency (KGE), and Root mean square error (RMSE) were used as the comparative indices between Gradient Boosting Algorithms and Adaptive Network-based Fuzzy Inference Systems. Results of the study showcased that both systems can simulate river flows as a function of catchment rainfalls; however, the Cat gradient Boosting algorithm (CatBoost) has a computational edge over the Adaptive Network Based Fuzzy Inference System (ANFIS). The CatBoost algorithm outperformed other algorithms used in this study, with the best correlation score for the testing dataset having 0.9934. The extreme gradient boosting (XGBoost), Light gradient boosting (LightGBM), and Ensemble models scored 0.9283, 0.9253, and 0.9109, respectively. However, more applications should be investigated for sound conclusions.

Application of Soft Computing Techniques in River Flow Modeling

Modeling of data is critical in the analysis and evaluation of hydrological behavior. River flow data is one of the most important data in explaining hydrology. Management of water resources; It takes place in the literature as an area that needs to be investigated in order to provide early warning for undesirable situations such as floods and drought. For this reason, it is of important to develop different techniques for the estimation and modeling of river flow or to make comparisons between techniques. In this study, the flow data of fourteen stations located in the Euphrates-Tigris basin between 1981 and 2010 were used. Adaptive Network Based Fuzzy Inference Systems (ANFIS), Support Vector Machine (SVM) techniques that are frequently used in the literature, and newly introduced Gaussian Process Regression (GPR), Extreme Learning Machine (ELM) and Emotional Neural Network (ENN) artificial intelligence techniques are compared. In addition, considering all performance indices, it was determined which technique gave better results with rank analysis. Although all models worked well, it was seen that the methods were ranked as ELM, GPR, ENN, SVM and ANFIS starting from the best. This has shown that ELM, GPR and ENN methods, which have been used recently in flow modeling, give better results than traditional methods with complex structures. In addition, flow values were used in the whole study and these values were examined in 3 different combinations. It was seen that the model structure that gave the best performance was the model structure that used the flow data from one, two and three days ago as an estimator. The results were analyzed with a Taylor diagram and time series graphs.

ANFIS-BASED REAL-TIME POWER ESTIMATION FOR WIND TURBINES

In this study, it is aimed to make real-time power estimation for the V44-600 model wind turbine of Vestas company. The scope of the study is aimed to perform ANFIS-based power estimation for the V44-600 VESTAS wind turbine, which is intensely used in the wind industry, by using the wind speed and air density data of the city of Nevşehir. For this purpose, an Adaptive Network Based Fuzzy Inference System (ANFIS) trained on V44-600 wind turbine data was used. For the training and testing steps of ANFIS, wind speed, air density, and output power of the wind turbine are used as input-output parameters. As a result of the simulations and training, the percent relative error value in the widest range where the prediction value deviates from the true value is 11.86%. This value was higher than expected due to the scarcity of the data used in the ANFIS training (144) and the repetitive values in the output power. Similarly, the lowest efficiency value is 89.4%. Despite all this, it has been observed that ANFIS gives good results if the data used in the testing process is within the scope of the data used in the training. Moreover, the developed model when supported with 32-bit hardware can make real-time power estimation for a real wind turbine. The main motivation for this study; is develop a model that can predict the output power for the Vestas V44-600 model based on wind speed and air density data. In addition, it is to produce the Fuzzy Interface System (FIS) file that enables the developed model to run on embedded systems.

Reliability analysis of reinforced soil slope stability using GA-ANFIS, RFC, and GMDH soft computing techniques

Soil is a heterogeneous medium, the characteristics that determine soil slope stability are highly variable, making the analysis a difficult task. The present research approach is switching from deterministic to probabilistic in order to account for the variability in soil properties. This research presents the use of three soft-computing techniques to evaluate reinforced soil slope reliability based on slope stability: Genetic Algorithm based Adaptive Network based Fuzzy Inference System (GA-ANFIS), Random Forests Classifier (RFC), and Group Method of Data Handling (GMDH). Shear strength parameters c (cohesion), ϕ (angle of shearing resistance) and ϒ (unit weight) are used as input variables, while Factor of Safety of Reinforced Soil Slope (F) is used as an output variable to determine the stability of a soil slope of a certain height. The Models were also evaluated using various assessment parameters and GA-ANFIS outperformed having some testing outputs as NS= 0.997, RMSE= 0.017, VAF= 99.731 %, Bias Factor= 1.002, PI= 1.998, R2 = 0.997, GPI= 6.6E-08, U95 = 0.627, tstat= 0.247 and β = 1.543. The GA-ANFIS model outperformed the GMDH and RFC models, according to the findings of the analyses using Taylor diagram and ROC curve. As a result, the GA-ANFIS model can be utilized as a reliable soft computing technique to analyze reinforced soil slope stability.

ANFIS-based Parameter Estimation of a Single Phase Inverter Circuit with Isolation Transformer

In this study, it is aimed to isolate the output interface of single-phase inverter circuits and the grid from each other. For this purpose, three-dimensional isolation transformers are designed with a software using the In order to determine the leakage inductance behavior of the isolation transformer, whose electromagnetic modeling was carried out with Ferrite N87 core material, the geometric distances of the primary-secondary windings and the diameter sizing of the windings were changed in parametric linear steps, and a data set was obtained as a specific parametric scenario for the leakage inductance values. Thus, the estimation process of the electromagnetic modelling of the transformer has been successfully carried out thanks to the training and testing processes of Adaptive Network Based Fuzzy Inference Systems (ANFIS) with the numerical information obtained from the finite element analysis (FEA) parametric data set. After the estimation processes, the percentage error is calculated as 0.3470% and 0.4448% for training and testing. Thus, the determination of the isolation transformer with the optimum values designed for the inverter circuit has become easier. Also, experimental analysis is performed on inverters to prove the robustness of the proposed method. In this context, first of all, RMS values that vary according to the different operating parameters of the inverters are created. The proposed ANFIS based system estimates RMS values with 7,057 % error.

A novel comparative model proposal for hospital pneumatic systems

Hospital pneumatic systems are of great importance in terms of efficient use of resources and early diagnosis of diseases. Each sample is transported at a constant and specific speed/pressure in existing pneumatic systems. This study aims to provide pressure/speed control according to the type of material being transported, prevent deterioration during transportation, and make a positive contribution to system users. We have proposed a new control model for hospital pneumatic systems. This hospital pneumatic system is modeled using fuzzy logic, adaptive neural network-based fuzzy inference system (ANFIS), and artificial neural network (ANN) methods. The parameters affecting the performance of the system are determined by experimental trials carried out. Fuzzy logic, ANFIS, and ANN models are compared using the regression coefficient (R2), Mean Absolute Deviation (MAD), and Root Mean Square Error (RMSE) metrics to determine the most suitable modeling method for the system. As a result of this study, carrying the pressure value (velocity) ideal for the sample type will increase the energy efficiency of the system.

Advanced Control Algorithm for FADEC Systems in the Next Generation of Turbofan Engines to Minimize Emission Levels

New propulsion systems in aircrafts must meet strict regulations and emission limitations. The Flightpath 2050 goals set by the Advisory Council for Aviation Research and Innovation in Europe (ACARE) include reductions of 75%, 90%, and 65% in CO2, NOx, and noise, respectively. These goals are not fully satisfied by marginal improvements in gas turbine technology or aircraft design. A novel control design procedure for the next generation of turbofan engines is proposed in this paper to improve Full Authority Digital Engine Control (FADEC) systems and reduce the emission levels to meet the Flightpath 2050 regulations. Hence, an Adaptive Network–based Fuzzy Inference System (ANFIS), nonlinear autoregressive network with exogenous inputs (NARX) techniques, and the block-structure Hammerstein–Wiener approach are used to develop a model for a turbofan engine. The Min–Max control structure is chosen as the most widely used practical control algorithm for gas turbine aero engines. The objective function is considered to minimize the emission level for the engine in a pre-defined maneuver while keeping the engine performance in different aspects. The Genetic Algorithm (GA) is applied to find the optimized control structure. The results confirm the effectiveness of the proposed approach in emission reduction for the next generation of turbofan engines.

Prediction of soil-bearing capacity on forest roads by statistical approaches.

The soil-bearing capacity is one of the important criteria in dimensioning the superstructure. In Turkey, predictability of California Bearing Ratio values, which may be used in the planning and dimensioning of forest roads, of which about 26% lacks the superstructure, by using soil mechanical properties (cost and time efficient parameters that are easier to determine) is investigated. Simple linear regression, multiple linear regression, artificial neural networks and adaptive network-based fuzzy inference system methods were utilized. Two hundred sixty-four California Bearing Ratio values obtained from the project carried out on the forest roads of Bartin Forest Operation Directorate were used in both the production of training-test data and the creation of models. Statistical performance of the models was assessed by means of parameters such as root-mean-square error, mean absolute error and R2. The obtained results show that the bearing capacity values predicted by artificial neural networks and adaptive network based fuzzy inference system models display significantly better performance than the simple linear regression and multiple linear regression models. While the highest prediction capacity belongs to adaptive network based fuzzy inference system (0.969-0.991), it is followed by artificial neural networks (R2 = 0.796-0.974), multiple linear regression (R2 = 0.796) and simple linear regression (R2 = 0.554). What makes the algorithms superior than the traditional statistical models is the fact that they have many processing neurons, each with local connections, and thus have higher error tolerance. On the other hand, for the forest and rural roads, which play an important role in rural development of the forest peasants, to be able to operate all-seasons, superstructure should be immediately built in order to minimize the wear on the roads.

Computational predictions for predicting the performance of steel 1 panel shear wall under explosive loads

Purpose The resistance of steel plate shear walls (SPSW) under explosive loads is evaluated using nonlinear FE analysis and surrogate methods. This study uses the conventional weapons effect program (CONWEP) model for the explosive load and the Johnson-Cook model for the steel plate. Based on the Taguchi method, 25 samples out of 100 samples are selected for a parametric study where we predict the damaged zones and the maximum deflection of SPSWs under explosive loads. Then, this study uses a multiple linear regression (MLR), multiple Ln equation regression (MLnER), gene expression programming (GEP), adaptive network-based fuzzy inference (ANFIS) and an ensemble model to predict the maximum detection of SPSWs. Several statistical parameters and error terms are used to evaluate the accuracy of the different surrogate models. The results show that the cross-section in the y-direction and the plate thickness have the most significant effects on the maximum deflection of SPSWs. The results also show that the maximum deflection is related to the scaled distance, i.e. for a value of 0.383. The ensemble model performs better than all other models for predicting the maximum deflection of SPSWs under explosive loads. Design/methodology/approach The SPSW under explosive loads is evaluated using nonlinear FE analysis and surrogate methods. This study uses the CONWEP model for the explosive load and the Johnson-Cook model for the steel plate. Based on the Taguchi method, 25 samples out of 100 samples are selected for a parametric study where we predict the damaged zones and the maximum deflection of SPSWs under explosive loads. Then, this study uses a MLR, MLnER, GEP, ANFIS and an ensemble model to predict the maximum detection of SPSWs. Several statistical parameters and error terms are used to evaluate the accuracy of the different surrogate models. The results show that the cross-section in the y-direction and the plate thickness have the most significant effects on the maximum deflection of SPSWs. The results also show that the maximum deflection is related to the scaled distance, i.e. for a value of 0.383. The ensemble model performs better than all other models for predicting the maximum deflection of SPSWs under explosive loads. Findings The resistance of SPSW under explosive loads is evaluated using nonlinear FE analysis and surrogate methods. This study uses the CONWEP model for the explosive load and the Johnson-Cook model for the steel plate. Based on the Taguchi method, 25 samples out of 100 samples are selected for a parametric study where we predict the damaged zones and the maximum deflection of SPSWs under explosive loads. Then, this study uses a MLR, MLnER, GEP, ANFIS and an ensemble model to predict the maximum detection of SPSWs. Several statistical parameters and error terms are used to evaluate the accuracy of the different surrogate models. The results show that the cross-section in the y-direction and the plate thickness have the most significant effects on the maximum deflection of SPSWs. The results also show that the maximum deflection is related to the scaled distance, i.e. for a value of 0.383. The ensemble model performs better than all other models for predicting the maximum deflection of SPSWs under explosive loads. Originality/value The resistance of SPSW under explosive loads is evaluated using nonlinear FE analysis and surrogate methods. This study uses the CONWEP model for the explosive load and the Johnson-Cook model for the steel plate. Based on the Taguchi method, 25 samples out of 100 samples are selected for a parametric study where we predict the damaged zones and the maximum deflection of SPSWs under explosive loads. Then, this study uses a MLR, MLnER, GEP, ANFIS and an ensemble model to predict the maximum detection of SPSWs. Several statistical parameters and error terms are used to evaluate the accuracy of the different surrogate models. The results show that the cross-section in the y-direction and the plate thickness have the most significant effects on the maximum deflection of SPSWs. The results also show that the maximum deflection is related to the scaled distance, i.e. for a value of 0.383. The ensemble model performs better than all other models for predicting the maximum deflection of SPSWs under explosive loads.

A novel approach for brain tumor detection by self-organizing map (SOM) using adaptive network based fuzzy inference system (ANFIS) for robotic systems

PurposeOne of the foremost research disciplines in medical image processing is to identify tumors, which is a challenging task practicing traditional methods. To overcome this, various research studies have been done effectively.Design/methodology/approachMedical image processing is evolving swiftly with modern technologies being developed every day. The advanced technologies improve medical fields in diagnosing diseases at the more advanced stages and serve to provide proper treatment.FindingsEither the mass growth or abnormal growth concerning the cells in the brain is called a brain tumor.Originality/valueThe brain tumor can be categorized into two significant varieties, non-cancerous and cancerous. The carcinogenic tumors or cancerous is termed as malignant and non-carcinogenic tumors are termed benign tumors. If the cells in the tumor are healthy then it is a benign tumor, whereas, the abnormal growth or the uncontrollable growth of the cell is indicated as malignant. To find the tumor the magnetic resonance imaging (MRI) is carried out which is a tiresome and monotonous task done by a radiologist. In-order to diagnosis the brain tumor at the initial stage effectively with improved accuracy, the computer-aided robotic research technology is incorporated. There are numerous segmentation procedures, which help in identifying tumor cells from MRI images. It is necessary to select a proper segmentation mechanism to detect brain tumors effectively that can be aided with robotic systems. This research paper focuses on self-organizing map (SOM) by applying the adaptive network-based fuzzy inference system (ANFIS). The execution measures are determined to employ the confusion matrix, accuracy, sensitivity, and furthermore, specificity. The results achieved conclusively explicate that the proposed model presents more reliable outcomes when compared to existing techniques.

Prediction of coefficient of consolidation in soil using machine learning techniques

Coefficient of consolidation in the soil is the significant engineering properties and an important parameter for designing and auditing of geo-technical structures. Therefore, in this study, authors have proposed an efficient methodology to prediction the coefficient of consolidation using machine learning models namely Multiple Linear Regression (MLR), Artificial Neural Network (ANN), Support Vector Regression (SVR), and Adaptive Network based Fuzzy Inference System (ANFIS). Further, various feature selection techniques such as Least Absolute Shrinkage and Selection Operator algorithm (LASSO), Random Forests - Recursive Feature Elimination (RF-RFE), and Mutual information have also been applied. It has been observed that feature selection methods have enhanced the quality of prediction model by eliminating the irrelevant features and utilized only important features while building the prediction models. Experiments are performed on the dataset collected on the 534 soil samples from Ha Noi –Hai Phong highway project, Vietnam. Experimental results show the adequacy of the proposed model, and the hybrid approach ANFIS which is a fusion of ANN and fuzzy inference system includes complementary information of the uncertainty and adaptability. ANFIS along with LASSO feature selection method produces the coefficient of determination of 0.831 and thus provides the best prediction for the coefficient of consolidation of a soil as compared to other approaches.

Adaptive Network Based Fuzzy Inference System and the Future of Employability

Educational data is considered by researchers and data scientists as an indicator for the future predictions. The current research study aims for classifying IT alumni students into employed and unemployed. The data collected from two universities in Jordan. 781 of IT alumni students in two universities in Jordan participate in the current study. Three classifiers are compared to determine the most suitable one for predicting the future of IT students’ employability. The results show that Adaptive Network Based Fuzzy Inference System came as a suitable classifier for predicting IT students’ employment in Jordan. As gender, programming skills, and communication skills came as the most effective factors affecting IT recruitment field, a set of recommendations is presented to the ministry of higher education based on the significant factors affecting IT graduates employment. Keywords: employability, ANFIS, classification, data mining DOI: 10.7176/NCS/12-04 Publication date: January 31 st 2021

A Comparison of machine learning regression models for critical bus voltage and load mapping with regards to max reactive power in PV buses

The aim of this paper is to compare voltage and system loading mapping capabilities of a variety of regression algorithms, such as Adaptive Network based Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Decision Tree (DT). A voltage sensitivity matrix is generated from the power flow Jacobian matrix for a loading scenario near the unstable point. Principal Component Analysis (PCA) is used to separate the system, close to the critical point, in order to group the buses into coherent voltage controlling areas. For different reactive power injection scenarios, we have different bus voltages that can be mapped by the aforementioned regression algorithms. The algorithms are trained with limited amounts of data, in order to establish a fair comparison between them. The present work shows that ANFIS and KNN have a better performance in critical voltage and load prediction when compared to the rest. The academic IEEE 14 and 118 bus systems are employed with all its limits considered, so the results may be reproduced.

Data Modelling To Determine Room Rate with Adaptive Network Based Fuzzy Inference System And Particle Swarm Optimization

Determination of room rate in a hotel isinfluenced by two factors, namely internal andexternal. From an external perspective, PT. PIMhas eight competitor hotels that affect its roomrate. The Hotel Manager analyzes eachcompetitor's room rate changes to staycompetitive. Human analysis has severalshortcomings: subjectivity, fatigue andinconsistency. Then we need a decision supportor decision companion machine to determine theroom rate. ANFIS-PSO is a hybrid algorithmfrom the Adaptive neural network based fuzzyinference system (ANFIS) by utilizing ParticleSwarm Optimization (PSO) optimization.Traditional ANFIS is Gradient Decent (GD) asan algorithm for parameter optimization (model).This often happens to be stuck to get optimallocal results, to overcome this PSO is used as asolution. The results obtained from the ANFIS- PSO training contained a difference of Rp.3173,187 or a percentage of 1.34%. From themodeling obtained applied to the hotel PT.PIM,with the result of an increase in revenue of Rp.17,493,548. The conclusion obtained is thatANFIS-PSO can help managers to determine theroom rate by modeling data obtained from theANFIS-PSO method. Suggestion for thedevelopment of this research is that ANFIS-PSOhas a complex complexity of training algorithmsbecause there is a combination of twoalgorithms, so to make it better a differentalgorithm design is needed.

Soft-computing techniques for prediction of soils consolidation coefficient

Coefficient of consolidation (Cv) is an important parameter in the designing of civil engineering structures founded on soil. Determination of the Cv in the laboratory is beset with complexity, therefore several attempts have been made to correlate it with the index properties of soil. In this paper, various advanced soft computing approaches namely Biogeography-Based Optimization based Artificial Neural Networks (ANN-BBO), Artificial Neural Networks (ANN), Adaptive Network based Fuzzy Inference System (ANFIS), and Support Vector Machines (SVM) were applied for quick and accurate prediction of the Cv of soft soil. For this, data of engineering properties of soil of Ha Noi–Hai Phong highway project of Vietnam was utilized as a case study for training and validating the models. Data pre-processing techniques namely correlation matrix and Principal Component Analysis (PCA) were applied in order to identify relevant variables for reducing data dimension while doing predictive analysis. Validation of the models was performed using statistical criteria namely Coefficient of determination (R2), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Monte Carlo simulation method was performed to estimate the robustness of the models. Comparison of results of these models indicated that all the studied models performed well but performance of the ANN-BBO model (R2 = 0.965, RMSE = 0.149, and MAE = 0.108) is the best in predicting the Cv of soil compared with other models such as ANFIS (R2 = 0.921, RMSE = 0.222, and MAE = 0.182), ANN (R2 = 0.922, RMSE = 0.302, and MAE = 0.178), and SVM (R2 = 0.949, RMSE = 0.199, and MAE = 0.112). Therefore, ANN-BBO can be used for better prediction of the Cv based on limited engineering parameters of soil.

Development of advanced artificial intelligence models for daily rainfall prediction

Abstract In this study, the main objective is to develop and compare several advanced Artificial Intelligent (AI) models namely Adaptive Network based Fuzzy Inference System optimized with Particle Swarm Optimization (PSOANFIS), Artificial Neural Networks (ANN) and Support Vector Machines (SVM) for the prediction of daily rainfall in Hoa Binh province, Vietnam. For this, meteorological variable parameters such as maximum temperature, minimum temperature, wind speed, relative humidity and solar radiation were collected and used as input parameters and daily rainfall as an output parameter in the models. Validation of the developed models was achieved using various quality assessment criteria such as correlation coefficient (R) and Mean Absolute Error (MAE), Skill Score (SS), Probability of Detection (POD), Critical Success Index (CSI), and False Alarm Ratio (FAR). The results showed that all the AI models provided reasonable predictions of daily rainfall but the SVM was found to be the best method for predicting rainfall. This method was also found to be the most robust and efficient prediction model while taking into account of input variability using the Monte Carlo approach. This AI based study would be helpful in quick and accurate prediction of daily rainfall.

Soil Moisture Inversion Via Semiempirical and Machine Learning Methods With Full-Polarization Radarsat-2 and Polarimetric Target Decomposition Data: A Comparative Study

In this article, surface soil moisture was retrieved from Radarsat-2 and polarimetric target decomposition data by using semiempirical models and machine learning methods. The semiempirical models and machine learning techniques employed were Oh (1992), Dubois (1995), Oh (2004) and Generalized Regression Neural Network (GRNN), Least Squares - Support Vector Machine (LS-SVM), Extreme Learning Machine (ELM), Kernel based Extreme Learning Machine (KELM), Adaptive Network based Fuzzy Inference System (ANFIS), respectively. In addition, Yamaguchi, van Zyl, Freeman-Durden, H/A/α and Cloude polarimetric target decomposition methods were used in this study. For soil moisture inversion, firstly, preprocessing was applied to the Radarsat-2 image of two different dates with bare and moderately vegetated soil. Then, sigma nought coefficients and the polarimetric decomposition components were extracted as feature vector from preprocessed SAR image pixels corresponding to ground measured points. Lastly, sigma nought coefficients were used in semiempirical inversion models, and sigma nought coefficients and polarimetric decomposition components were used as input to machine learning methods. The best accuracy results for semiempirical models were 13.01 vol. % and 17.91 vol. % Root Mean Square Error (RMSE) for bare and moderately vegetated soil, respectively. The best accuracy for machine learning techniques were 4.04 vol. % and 2.72 vol. % RMSE for two dates, respectively. The results indicated that the machine learning techniques performed much better than the semiempirical models.

Different Autopilot Systems Design For a Small Fixed Wing Unmanned Aerial Vehicle

The aim of this study is to design an autopilot system for Unmanned Aerial Vehicle (UAV) in a small size and to compare the efficiency of the systems designed with different methods. For this purpose, linearized equations of motion for a small size UAV has beeen firstly obtained. Then the state space equations are used to check the stability characteristics of the UAV. Using the classical method, the longitudinal pitch angle, altitude and speed controller are designed for separate transfer functions according to the location curve of the roots and the desired response values. PID values were determined by considering the response of the system using classical methods. On the other hand, a feedback control system has been designed to improve the stability of different lines. And also an orientation controller has been designed. New approach proposed in the study is Adaptive Network Based Fuzzy Inference Systems (ANFIS) controller. In this study, a new controller approach based on fuzzy logic is proposed. Four data sets, PID control inputs and control sign have been obtained for the design of the proposed controller. These data sets have been used for training the fuzzy controller. As a result; It is presented with graphs that the proposed method is applicable and gives successful results.

Fault disturbances classification analysis using adaptive neuro-fuzzy inferences system

This paper affords the use of neuro-fuzzy technique called the Adaptive Network–based Fuzzy Inference System (ANFIS) to highlight its ability to perform fault disturbances classification tasks using extracted features based on S-transforms methods. The ANFIS model with a five-layered architecture was trained using extracted features to classify signal data comprising various faults disturbances, namely, voltage sag, swell, impulsive, interruption, notch, and pure signal. Results obtained showed that the ANFIS model is very suitable and can generate excellent classification results provided that the right type and number of Membership Functions (MFs) are used in the classification task.