Network-based Fuzzy Inference System Research Articles

Neuro Fuzzy Technique for Steel Process Modeling

Steels are composites of carbon and iron. The steel properties extremely rely on the amount of alloying constituents, so their quantities are carefully measured and controlled during its production. The addition of alloying components in the manufacturing of steel is usually made taking into account the expert experience of specialists and in this manner an administrator decides the amount of alloying components to be added to create the steel of a specific kind. Since the ratio of alloying elements in steel manufacturing normally has indefinite nature, Subtractive Clustering method and Adaptive Network based Fuzzy Inference System (ANFIS) is projected in this work for evaluating the amount of alloying elements with reduced computation errors. The optimal fuzzy rules are evaluated using subtractive clustering and ANFIS method which includes the advantages of fuzzy systems and the neural networks is used to adjust the acquired fuzzy rules produced by means of subtractive clustering. The simulation outcomes signify that the proposed methodology may be implemented effectually for making steel.

Application of Soft Computing Models to Daily Average Temperature Analysis

Providing critical information about daily life, weather forecasting has important role for human being. Especially, temperature forecasting is rather important because it affects not only people but also other atmospheric parameters. Various techniques have been used for analysis of the dynamic behaviour of weather. This ranges from simple observation of weather to using computer technology. In this study, ANFIS (Adaptive Network Based Fuzzy Inference System), ANN (Artificial Neural Network) and MRA (Multiple Regression Analysis) have been applied for weather forecasting. To judge the forecasting capability of the proposed models, the graphical analysis and the indicators of the accuracy of Mean Absolute Deviation (MAD), Mean Square Error (MSE), Root-Mean Squared Error (RMSE), Mean Absolute Percent Error (MAPE), Determination Coefficient (R 2 ), Index of Agreement (IA), Fractional Variance (FV), Coefficient of Variation (CV, %) are given to describe models’ forecasting performance and the error. The results show that ANFIS exhibited best forecasting performance on weather forecasting compared to ANN and MRA.

Comparison of ANFIS and ARIMA Model for Weather forecasting

In this paper quickly illustrate the correlation investigation of Auto-Regressive Integrated Moving and Average (ARIMA) and Adaptive Network Based Fuzzy Inference System (ANFIS) models done by climate estimating .The climate determining is taken from University of Waterloo. The information is taken as Relative Humidity, Ambient Air Temperature, Barometric Pressure and Wind Direction utilized within this paper. The paper is carried out by analyzing the exhibitions are seen by demonstrating of ARIMA and ANIFIS model like with Sum of average of errors. Versatile Network Based Fuzzy Inference System (ANFIS) demonstrating is carried out by Mat lab programming and Auto-Regressive Integrated Moving and Average (ARIMA) displaying is produced by utilizing XLSTAT programming. ANFIS is carried out in Fuzzy Logic Toolbox in Mat Lab. Keywords: ARIMA, ANFIS, Fuzzy Logic Tool, Weather Forecasting, XLSTAT Software

Network-based Type-2 Fuzzy System with Water Flow Like Algorithm for System Identification and Signal Processing

This paper introduces a network-based interval type-2 fuzzy inference system (NT2FIS) with a dynamic solution agent algorithm water flow like algorithm (WFA), for nonlinear system identification and blind source separation (BSS) problem. The NT2FIS consists of interval type-2 asymmetric fuzzy membership functions and TSK-type consequent parts to enhance the performance. The proposed scheme is optimized by a new heuristic learning algorithm, WFA, with dynamic solution agents. The proposed WFA is inspired by the natural behavior of water flow. Splitting, moving, merging, evaporation, and precipitation have all been introduced for optimization. Some modifications, including new moving strategies, such as the application of tabu searching and gradient-descent techniques, are proposed to enhance the performance of the WFA in training the NT2FIS systems. Simulation and comparison results for nonlinear system identification and blind signal separation are presented to illustrate the performance and effectiveness of the proposed approach.

English

In this paper, adaptive network based fuzzy inference system (ANFIS) was used in control applications of a Heat Exchanger as interval type-2 fuzzy logic controller (IT-2FL). Two adaptive networks based fuzzy inference systems were chosen to design type-2 fuzzy logic controllers for each control applications. The whole integrated system for control of Heat Exchanger is called IT2 FLC+ANFIS controller. Membership functions in interval type-2 fuzzy logic controllers were set as an area called footprint of uncertainty (FOU), which is limited by two membership functions of adaptive network based fuzzy inference systems; they were upper membership function (UMF) and lower membership function (LMF). This work deals with the design and application of an IT2 FLC+ANFIS controller for a heat exchanger. To deal with the problem of parameter adjustment, efficient neuro-fuzzy scheme known as the ANFIS (Adaptive Network-based Fuzzy Inference System) can be used. The IT2 FLC+ANFIS controller of the heat exchanger is compared with classical PID control. System behaviors were defined by Lagrange formulation and MATLAB computer simulations. The simulation results confirm that interval type2 fuzzy is one of the possibilities for successful control of heat exchangers. The advantage of this approach is that it is not a linear-model-based strategy. Comparison of the simulation results obtained using IT2 FLC+ANFIS controller and those obtained using classical PID control demonstrates the effectiveness and superiority of the proposed approach because of the smaller consumption of the heating medium

Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS

Abstract Interests in Closed-Loop Supply Chain (CLSC) issues are growing day by day within the academia, companies, and customers. Many papers discuss profitability or cost reduction impacts of remanufacturing, but a very important point is almost missing. Indeed, there is no guarantee about the amounts of return products even if we know a lot about demands of first products. This uncertainty is due to reasons such as companies’ capabilities in collecting End-of-Life (EOL) products, customers’ interests in returning (and current incentives), and other independent collectors. The aim of this paper is to deal with the important gap of the uncertainties of return products. Therefore, we discuss the forecasting method of return products which have their own open-loop supply chain. We develop an integrated two-phase methodology to cope with the closed-loop supply chain design and planning problem. In the first phase, an Adaptive Network Based Fuzzy Inference System (ANFIS) is presented to handle the uncertainties of the amounts of return product and to determine the forecasted return rates. In the second phase, and based on the results of the first one, the proposed multi-echelon, multi-product, multi-period, closed-loop supply chain network is optimized. The second-phase optimization is undertaken based on using general exact solvers in order to achieve the global optimum. Finally, the performance of the proposed forecasting method is evaluated in 25 periods using a numerical example, which contains a pattern in the returning of products. The results reveal acceptable performance of the proposed two-phase optimization method. Based on them, such forecasting approaches can be applied to real-case CLSC problems in order to achieve more reliable design and planning of the network

Estimation of the Change in Lake Water Level by Artificial Intelligence Methods

In this study, five different artificial intelligence methods, including Artificial Neural Networks based on Particle Swarm Optimization (PSO-ANN), Support Vector Regression (SVR), Multi- Layer Artificial Neural Networks (MLP), Radial Basis Neural Networks (RBNN) and Adaptive Network Based Fuzzy Inference System (ANFIS), were used to estimate monthly water level change in Lake Beysehir. By using different input combinations consisting of monthly Inflow - Lost flow (I), Precipitation (P), Evaporation (E) and Outflow (O), efforts were made to estimate the change in water level (L). Performance of models established was evaluated using root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2). According to the results of models, e-SVR model was obtained as the most successful model to estimate monthly water level of Lake Beysehir.

Modeling of TiN Coating Thickness Using ANFIS

In this paper, an approach in predicting thickness of Titanium Aluminum Nitrite (TiN) coatings using Adaptive Network Based Fuzzy Inference System (ANFIS) is implemented. The TiN coatings were coated on tungsten carbide (WC) using Physical Vapor Deposition (PVD) magnetron sputtering process. The N2 pressure, argon pressure and turntable speed were selected as the input parameters and the coating thickness as an output of the process. Response Surface Methodology (RSM) was used to design the matrix in collecting the experimental data. In the ANFIS structure, three bell shapes were used as input membership function (MFs). The collected experimental data was used to train the ANFIS model. Then, the ANFIS model was validated with confirmatory test data and compared with other prediction models in terms of the root mean square error (RMSE), residual error and prediction accuracy. The result indicated that the developed ANFIS model result was the lowest RMSE7 and average residual error, besides the highest in prediction accuracy compared to the other models. The result also indicated that the limited experimental data could be used in training the ANFIS model and resulting accurate predictive result.

Modeling and OnLine Control of Nonlinear Systems using Neuro-Fuzzy Learning tuned by Metaheuristic Algorithms

This paper proposes a hybrid learning algorithm for tuning an Adaptive Network based Fuzzy Inference System ANFIS. The proposed scheme of adapting parameters in ANFIS employs evolutionary techniques PSO and GA to adjust the antecedent parameters. The leastsquares (LSE) algorithm is used to adjust consequent parameters. The number of fuzzy rules is fixed and given by using the Xie Beni’s index. This new approach is applied to identify and control nonlinear systems with an on-line strategy. The obtained results are compared to similar ANFIS using gradient descent method GD as antecedent parameters of training algorithm and other methods applied in the same problems.

ANFIS based prediction of the aluminum extraction from boehmite bauxite in the Bayer process

Abstract This paper presents the results of nonlinear statistical modeling of the bauxite leaching process, as part of Bayer technology for alumina production. Based on the data, collected during the year 2011 from the industrial production in the alumina factory Birač, Zvornik (Bosnia and Herzegovina), nonlinear statistical modeling of the industrial process was performed. The model was developed as an attempt to define the dependence of the Al2O3 degree of recovery as a function of input parameters of the leaching process: content of Al2O3, SiO2 and Fe2O3 in the bauxite, as well as content of Na2Ocaustic and Al2O3 in the starting sodium aluminate solution. As the statistical modeling tool, Adaptive Network Based Fuzzy Inference System (ANFIS) was used. The model, defined by the ANFIS methodology, expressed a high fitting level and accordingly can be used for the efficient prediction of the Al2O3 degree of recovery, as a function of the process inputs under the industrial conditions.

ANFIS Modeling For a Water-Methanol System in a Spiral Plate Heat Exchanger

In this paper, an experimental investigation of Water- Methanol system in a Spiral plate Heat Exchanger (SHE) is presented. Experiments have been conducted by varying the mass flow rate of cold fluid (Methanol), the mass flow rate of hot fluid (Water) and inlet temperature of the hot fluid, by keeping inlet temperature of the cold fluid constant. The effects of relevant parameters on the performance of spiral plate heat exchanger are studied. Also, in this paper, an attempt is made to propose Adaptive Network based Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) models for the analysis of SHE. The data required to train the models are obtained from the experimental data based on Response Surface Methodology (RSM). The ANN models are developed using Back Propagation Network (BPN) algorithm, incorporating Levenberg-Marquardt (L-M) training method. The ANFIS models are developed based on advanced neural- fuzzy technology. The ANFIS model possesses the robustness of fuzzy system, the learning ability of neural networks and can adapt to various situations. The accuracy of the trained networks are verified according to their ability to predict unseen data by minimizing root mean square error (RMSE), average percentage error (APE) and correlation coefficient (%R 2 ) value. The prediction of the parameters can be obtained without using charts and complicated equations. The data obtained from ANFIS and ANN models for overall heat transfer coefficient (U) and pumping power (Wp) are compared with those of experimental data. It is observed that the accuracy between the ANFIS model's predictions, NN model's predictions and experimental values are achieved with minimum % error, APE, RMSE and Correlation coefficient (%R 2 ). Also it is proved that ANFIS models give better performance

Predicting Mathematics 1 Course Success by Using Hierarchical Adaptive Network Based Fuzzy Inference System

Predicting Mathematics 1 course success of students is very important to prepare them before the semester. It is difficult to obtain solution because of the non-linear property of data set. Fuzzy logic is one of the common methods for the problems which involve numeric values. In fuzzy logic, it is important to determine membership functions and their parameter's values correctly. This can be done by an expert or can be learned with a data set. In this study, we aimed to predict the Mathematics 1 course success of 434 students who enrolled to Engineering Faculty of Pamukkale University in 2007-2008 academic year by using their university exam data. For this, the adaptive-network-based fuzzy inference system (ANFIS) which combines the important characteristics of artificial neural network and fuzzy logic was used. In training section, nine parameters which are selected from sixteen parameters in data set with different combinations were given to the ANFIS. When an ANFIS structure with nine input parameters has at least three membership functions for each input, it will have at least 3^9 fuzzy rules. Because of this, the training part is too slow and too much memory is needed. Instead of this inefficient structure, a hierarchical method was proposed. In this method, the ANFIS is partitioned to the sub-systems. Each sub-system performs some part of input parameters and sends their result to the final ANFIS structure to obtain the overall system output. After testing with one-third of data set, two best prediction results with ratio 77.77% and 78.47% are obtained. When these results are analyzed, it is seen that 64 successful students from 85 students and 48 unsuccessful students from 59 students in Mathematics 1 course were predicted truly in the result with ratio 77.77%. Similarly, 69 successful students from 85 students, and 44 unsuccessful students from 59 students were predicted truly in the result with ratio 78.47%.

DETERMINATION OF REFLECTANCE VALUES OF HYPERICUM'S LEAVES UNDER STRESS CONDITIONS USING ADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM

The effects of water stress and salt levels on hypericum's leaves were examined on greenhouse-grown plants of Hypericum perforatum L. by spectral reflectance. Salt levels and irrigation levels were applied 0, 1, 2.5 and 4 deci Siemens per meter (dS/m), 80%, 100% and 120% respectively. Adaptive Network based Fuzzy Inference System (ANFIS) was performed to estimate the effects of water stress and salt levels on spectral reflectance. As a result of ANFIS, it was found that there was close relationship between actual and predicted reflectance values in Hypericum perforatum L. leaves. Performance of ANFIS was examined under different numbers of epoch and rules. On the other hand, RMSE, correlation and analysis time values were found as outputs. Correlation was 99%. The estimation of optimal ANFIS model was determined in 3*3*3 number of rules with 400 epochs.

Age Prediction and Performance Comparison by Adaptive Network based Fuzzy Inference System using Subtractive Clustering

Age Prediction and Performance Comparison by Adaptive Network based Fuzzy Inference System using Subtractive Clustering

Voltage Sag and Swell Estimation Using ANFIS for Power System Applications

Power quality is a term that is now extensively used in power systems applications, and in this context the voltage, current, and phase angle are discussed widely. In particular, different algorithms that are capable of detecting the voltage sag and swell information in a real time environment have been proposed and developed. Voltage sag and swell play an important role in determining the stability, quality, and operation of a power system. This paper presents ANFIS (Adaptive Network based Fuzzy Inference System) models with different membership functions to build the voltage shape with the knowledge of known system parameters, and detect voltage sag and swell accurately. The performance of each method has been compared with each other/other methods to determine the effectiveness of the different models, and the results are presented.

Improved prediction of mental workload versus HSE and ergonomics factors by an adaptive intelligent algorithm

This study proposes an adaptive intelligent algorithm for forecasting and improving mental workload among operators with respect to HSEE in large gas Treatment Company. The algorithm is composed of reliability, verification, validation and forecasting mechanisms through robust mathematical and statistical methods, Artificial Neural Network (ANN) and Adaptive Network Based Fuzzy Inference System Algorithm (ANFIS). To achieve the objectives of this study, standard questionnaires with respect to HSEE are completed by operators. The average results for each category of HSEE are used as inputs and mental workload is used as output in the algorithm. The efficiency of ANN is examined against ANFIS by use of Mean Absolute Percentage Error (MAPE). The results concluded that ANN provides better solutions than ANFIS. Therefore, ANN is used to forecast and rank operators performance with respect to HSEE and mental workload. Normal probability technique is used to identify outlier operators. Moreover, operators with inadequate mental workload with respect to HSEE are identified. This would help managers to foresee if operators are satisfied with their mental workload in the context of HSEE. Finally, sensitivity analysis of the intelligent algorithm is shown by error analysis in contrast with conventional regression approaches. This is the first study that introduces an adaptive algorithm for efficient forecasting of mental workload with respect to HSEE program in complex systems.

Synchronization controller for a 3-R planar parallel pneumatic artificial muscle (PAM) robot using modified ANFIS algorithm

Parallel manipulator is a closed-kinematic chain mechanism in which performance of its end effector – moving platform is contributed by its independent actuators. In traditional designs, each elemental actuator has its own controller as well as reference input, and it works independently without gathering information from its neighbors. Consequently, as one of the actuators cannot keep up with the others, the platform performance is easily deteriorated due to the lack of coherence between these actuators. Therefore, the aim of this paper is to design a 3-R planar parallel robot and develop a proper synchronization controller for its tracking control task. Adaptive Network Based Fuzzy Inference System (ANFIS) algorithm was modified and applied as the main strategy of this synchronization controller. The controller is then able to compensate errors between the actuators and enforce them to cooperate harmonically with each other regardless external disturbances caused by the outside environment or geometrical constraints of the closed-loop structure. Simulations and practical experiments on a scaled parallel robot were carried out to evaluate the designed controller. The results showed that by applying the proposed control technique, the working errors of the component actuators converged quickly to zero almost at the same time. As a result, the tracking performance of the common platform was significantly improved in comparison with the performance when applying a non-synchronization controller. The proposed method is effective in controlling systems which require collaborations between the sub-agents.

Automatic RNA virus classification using the Entropy-ANFIS method.

Innovations in the fields of medicine and medical image processing are becoming increasingly important. Historically, RNA viruses produced in cell cultures have been identified using electron microscopy, in which virus identification is performed by eye. Such an approach is time consuming and depends on manual controls. Moreover, detailed knowledge about RNA viruses is required. This study introduces the Entropy-Adaptive Network Based Fuzzy Inference System (Entropy-ANFIS method), which can be used to automatically detect RNA virus images. This system consists of four stages: pre-processing, feature extraction, classification and testing the Entropy-ANFIS method with respect to the correct classification ratio. In the pre-processing stage, a center-edge changing method is used, in which the Euclidian distances are calculated from the center pixels to the edges of the imaged object. In this way, the distance vector is obtained. This calculation is repeated for each RNA virus image. In feature extraction, stage norm entropy, logarithmic energy and threshold entropy values are calculated to form the feature vector. The obtained feature vector is independent of the rotation and scale of the RNA virus image. In the classification stage, the feature vector is given as input to the ANFIS classifier, ANN classifier and FCM cluster. Finally, the test stage is performed to evaluate the correct classification ratio of the Entropy-ANFIS algorithm for the RNA virus images. The correct classification ratio has been determined as 95.12% using the proposed Entropy-ANFIS method.

Application of Soft Computing Tools for Wave Prediction at Specific Locations in the Arabian Sea Using Moored Buoy Observations

The knowledge of design and operational values of significant wave heights is perhaps the single most important input needed in ocean engineering studies. Conventionally such information is obtained using classical statistical analysis and stochastic methods. As the causative variables are innumerable and underlying physics is too complicated, the results obtained from the numerical models may not always be very satisfactory. Soft computing tools like Artificial Neural Network (ANN) and Adaptive Network based Fuzzy Inference System (ANFIS) may therefore be useful to predict significant wave heights in some situations. The study is aimed at forecasting of significant wave height values in real time over a period of 24hrs at certain locations in Indian seas using the models of ANN and ANFIS. The data for the work were collected by National Institute of Ocean Technology, Chennai. It was found that the predictions of wave heights can be done by both methods with equal efficiency and satisfaction.

Application of adaptive network based fuzzy inference system method in economic welfare

Welfare analysis is a very important issue in the context of socio-economic. There is a huge literature about economic welfare and exist many conventional techniques and traditional methods that are used in almost all economic welfare studies. Regardless of the method used in welfare studies, the key element to promote the welfare of households in a community is to identify factors affecting welfare. For the first time, Adaptive Network Based Fuzzy Inference System (ANFIS) which is a powerful artificial intelligence tool is used as a new approach in economic welfare. To do this, real micro-data including some characteristics of households and housing units were used. We identified the most important factors affecting household welfare applying the ANFIS method. The ANFIS was then parameterized using these factors in order to predict the welfare measure. The empirical results showed that the ANFIS method outperforms the multiple regression method.