Neural Network Fuzzy Inference System Research Articles

The capability of coupled fuzzy logic and adaptive neural network in the formability prediction of steel sheets

In this study, an adaptive neural network fuzzy inference system (ANFIS) is employed to obtain a model demonstrating a cold rolling effect on the forming limits of sheet metals. Artificial intelligence-based methods require valid datasets for training and testing designed neural networks. In this regard, comprehensive experiments are conducted to achieve different thickness reductions in cold rolling for 304L sheet metals. The effect of cold rolling on the uniaxial tensile curves is determined experimentally. In addition, metallography and tensile tests are performed to determine the stretch in grains due to cold rolling. Moreover, experimental FLDs are obtained using the hemisphere punch test. The experimental data are further utilized to train and test the ANFIS. Subsequently, the model is used to predict variations of FLD for cold rolling thickness reduction. It is shown that with extremely lower computational cost in comparison to the experimental method, ANFIS can qualitatively predict the dependency of forming limits on the cold rolling thickness reduction.

Improvement of Rooftop Solar Panels Efficiency using Maximum Power Point Tracking Based on an Adaptive Neural Network Fuzzy Inference System

Rooftop solar panels are a strategy for achieving Indonesia's renewable energy goals, but their non-linear characteristics make them difficult to control, especially in the face of extreme weather changes. An effective controller is needed to optimize the power output of solar panels. This study proposes a Maximum Power Point Tracking (MPPT) controller based on an Adaptive Neural network Fuzzy Inference System (ANFIS) to address this control problem. The capacity of the rooftop solar panels is 3,430-Watt peak (Wp) and they are connected to a 220-Volt (V) grid system. The system is designed, simulated, and analyzed using the Simulink model. The proposed ANFIS MPPT control for rooftop solar panels is compared to Perturb and Observe (P&O) MPPT and no MPPT systems. The simulation results show that in rapid changes in irradiation and extreme temperature, the efficiency of MPPT based on ANFIS is better than P&O MPPT and no MPPT by 0.4523 and 0.1115%, respectively.

Adaptive fuzzy‐region growing fusion and improved CNN‐ANFIS‐based automated segmentation and classification of cervical cancer

SummaryThe cervical cancer screening mechanisms help to classify cervical lesions into cancerous, precancerous, and normal. But, some of the classification approaches provide optimal outcomes based on the precise spotting and segmenting of cervical cancer. Moreover, still, challenges persist in the precise spotting of cervical cancer. The conventional screening methods for cervical cancer require more experienced pathologists, but it may also lead to poor efficiency and low accuracy. To overcome these issues, medical image processing introduces both machine learning and deep learning methods into cervical cell classification. The major scope of this task is to plan a new cervical cancer diagnosis method using intelligent approaches. Here, the segmentation of abnormality is done by the novel adaptive fuzzy‐region growing fusion model, which is improved by the hybridized optimization algorithm named gray wolf‐tunicate swarm algorithm (GW‐TSA). Then, the new ensemble classification termed Improved convolutional neural network with artificial neural network fuzzy inference system, which depends on the proposed GW‐TSA for performance enhancement through optimizing the hyperparameters. Finally, the simulation findings show that the designed approach is achievable and provides the highest accuracy rate in diagnosing cervical cancer.

Effects of thickness reduction in cold rolling process on the formability of sheet metals using ANFIS

Cold rolling has detrimental effect on the formability of sheet metals. It is, however, inevitable in producing sheet high quality surfaces. The effects of cold rolling on the forming limits of stretch sheets are not investigated comprehensively in the literature. In this study, a through experimental study is conducted to observe the effect of different cold rolling thickness reduction on the formability of sheet metals. Since the experimental procedure of such tests are costly, an artificial intelligence is also adopted to predict effects of cold thickness reduction on the formability of the sheet metals. In this regard, St14 sheets are examined using tensile, metallography, cold rolling and Nakazima’s hemi-sphere punch experiments. The obtained data are further utilized to train and test an adaptive neural network fuzzy inference system (ANFIS) model. The results indicate that cold rolling reduces the formability of the sheet metals under stretch loading condition. Moreover, the tensile behavior of the sheet alters considerably due to cold thickness reduction of the same sheet metal. The trained ANFIS model also successfully trained and tested in prediction of forming limits diagrams. This model could be used to determine forming limit strains in other thickness reduction conditions. It is discussed that determination of forming limit diagrams is not an intrinsic property of a chemical composition of the sheet metals and many other factors must be taken into account.

A Comparative Analysis for Optical Character Recognition for Text Extraction from Images Using Artificial Neural Network Fuzzy Inference System

Artificial neural networks (ANN) has the capability to analyze raw data from processing input-output relationships. This function considers them important in areas of industry with such information is unusual. Researchers have tried to extract the information embedded within ANNs as set of rules used with inference systems to resolve the black-box function of ANNs. When ANN applied within a fuzzy inference system, the extracted rules yield high classification accuracy. In this paper a Multi-Layer Neural Feed-Forward Network using Artificial Neural Network Fuzzy Inference System (MLNFFN-ANNFIS) is proposed for accurate character recognition from images. The technique targets areas of business that have less complicated issues about which there is no simpler approach is desired to a complex one. This paper proposed an Optical Character Recognition model for Text Extraction from Images using Artificial Neural Network Fuzzy Inference System for accurate text detection from images. The technique proposed is more effective and simple than most of the techniques previously proposed. The proposed model is compared with various traditional models and the results indicate that the proposed model accuracy is more and performance is also improved.

Prediction of the optimal dosage of coagulants in water treatment plants through developing models based on artificial neural network fuzzy inference system (ANFIS).

Coagulation and flocculation are the prominent processes and unit-operations in water treatment plants. One of the most challenging operations in water treatment process is determining of the coagulant dose. The Jar-test method is usually used to determine the coagulant dose. Considering that this traditional method is time consuming, associated with human error and highly affected by raw water quality fluctuations. In this study, artificial fuzzy neural network (ANFIS) according to subtractive clustering (SUB) method was applied in order to determine the optimal dose of coagulant in the water treatment plants. Adopting SUB method tend to moderate the number of rules and the interconnections besides enhancing the model responsibility and smart model recognition. The amount of pH, turbidity of raw water influent, alkalinity, temperature, and electrical conductivity were collected as input data. The results of modeling by ANFIS with correlation coefficients of 0.85 and 0.84 and RMSE 1.32 and 1.83, respectively, for alum and polyaluminum chloride (PAC) coagulant dose, indicated that ANFIS is an effective method for determination of the optimal coagulation dose in the water treatment plant.

Passive Adaptive Network Fuzzy Inference Controller Basedwheeled Mobile Robot

Passive adaptive network fuzzy inference controllerby teleoperation based wheeled mobile robot navigationand wheel slippage has been envisaged in this paper. Apassivity controller mode is employed to render the obstacleavoidance for wheeled mobile robot thereby ensuring incidentfree navigation particularly unlimited workspace andsurface slippage of coordination of master robot positionand slave robot velocity. In order to achieve system stabilityeffective control strategies has been formulated. Thesestrategies bypass robot slippage and skidding due to softcomputing.Perfect position tracking of the master/ slaverobot with Simpson 1/3 rule based adaptive neural networkfuzzy inference system is reducing the error in terms ofunavoidable and acceptable forces, which is confirmed bypassivity. We demonstrated theoretically, the systems stabilityis shown via its passivity hybrid system in that force feltby the human operator is approximately equal to the forcesapplied by the environment force and sensor predictor ofslave robot, which is a satisfactory performance outcome.Simulation results verify the effectiveness of the proposedscheme for teleoperation under passivity controller.

RETRACTED ARTICLE: Computer aided innovation method for detection and classification of cervical cancer using ANFIS classifier

Early detection of cervical tumour is very important to minimise deaths due to cervical cancer. Further it provides a deep insight into the anatomical information of the normal and abnormal cervix and helps in planning for a good treatment well in advance. Numerous techniques are used to detect malignancy through image segmentation. One such segmentation technique is discussed here. The proposed technique uses Artificial Neural Network Fuzzy Inference system (ANFIS) and watershed segmentation techniques for image classification and processing and compares the results with known techniques. A comprehensive set of fuzzy rules was used in the experiment to classify abnormal images to the corresponding malignancy. The experiment shows that the proposed technique is feasible and provides greater accuracy in detection of tumour types.

Monitoring Distillation Column Systems Using Improved Nonlinear Partial Least Squares-Based Strategies

Fault detection in industrial systems plays a core role in improving their safety, productivity and avoiding expensive maintenance. This paper proposed and verified data-driven anomaly detection schemes based on a nonlinear latent variable model and statistical monitoring algorithms. Integrating both the suitable characteristics of partial least squares (PLS) and adaptive neural network fuzzy inference systems (ANFIS) procedure, PLS-ANFIS model is employed to allow for flexible modeling of multivariable nonlinear processes. Furthermore, PLS-ANFIS modeling was connected with k-nearest neighbors (kNN)-based data mining schemes and employed for nonlinear process monitoring. Specifically, residuals generated from the PLS-ANFIS model are used as the input to the kNN-based mechanism to uncover anomalies in the data. Moreover, kNN-based exponentially smoothing with parametric and nonparametric thresholds is adopted to better anomaly detection. The effectiveness of the proposed approach is evaluated using real measurements from an actual bubble cap distillation column.

Liver Lesions Diagnosis Using Gwo-Anfis Framework

In this paper, liver abnormality is detected using an improved classification model that consists of series of process. The study reveals the liver condition to be normal or abnormal using the proposed system. The study uses both structural and statistical analysis, where both these analysis is combined with the process of classification. Initially, the noises are removed using Impulse Noise Removal and then the Segmentation is carried out using Gray Wolf Optimisation (GWO) algorithm. After the segmentation, the features are extracted through Local Binary Patters (LBP) Operator and then Artificial Neural Network Fuzzy Inference System (ANFIS) classifies the liver regions as malignant or benign. Various images collected from laboratories are used in both training and testing stages. The results are validated in terms of two different texture feature extractors namely, GLCM and LBP. The result shows that the proposed classifier using GLCM classifier obtains improved classified patterns than the existing methods.

Adaptive neural network fuzzy inference system for HFC processes

The paper presents the design and implementation of a fuzzy inference system (FIS) trained with adaptive neural networks for the generation of specification references in high frequency current (HFC) hardening processes. The specification references are then further used for the control of the process in obtaining the desired outcomes in terms of material hardening and resistance. The FIS is trained using data obtained from experimentation on an industrial HFC device. The trained FIS is then compared to a manually tuned FIS, resulting from expert and operator designs. The results led to the development of intelligent control interfaces in real time through the ANFIS method.

Lung cancer segmentation and diagnosis of lung cancer staging using MEM (modified expectation maximization) algorithm and artificial neural network fuzzy inference system (ANFIS)

Medical image processing plays a major role in advancing treatment planning, identification of disease and guiding for surgery. Lung cancer is one among the dangerous diseases that leads to death of most human beings due to uncontrolled growth in the cell. This research area is finding more importance among researchers is that because the available methods for lung cancer detection are very painful. So the automatic detection of lung nodule of computer tomography (CT) is finding more importance among researchers. Though there are a number of methods available for segmentation of lung nodules each method has its drawbacks because of the divergence that exists in lung nodule. The proposed method is the detection of lung nodules using MEM algorithm and classification is based on Artificial Neural Network Fuzzy Inference System (ANFIS) experimental results illustrate in an improvement of accuracy and reduction of false positive results. A comparative analysis is performed with FCM and FLICM algorithms to show the superior nature of MEM.

Predicting Real-Time Crash Risk for Urban Expressways in China

We developed a real-time crash risk prediction model for urban expressways in China in this study. About two-year crash data and their matching traffic sensor data from the Beijing section of Jingha expressway were utilized for this research. The traffic data in six 5-minute intervals between 0 and 30 minutes prior to crash occurrence was extracted, respectively. To obtain the appropriate data training period, the data (in each 5-minute interval) during six different periods was collected as training data, respectively, and the crash risk value under different data conditions was defined. Then we proposed a new real-time crash risk prediction model using decision tree method and adaptive neural network fuzzy inference system (ANFIS). By comparing several real-time crash risk prediction methods, it was found that our proposed method had higher precision than others. And the training error and testing error were minimum (0.280 and 0.291, resp.) when the data during 0 to 30 minutes prior to crash occurrence was collected and the decision tree-ANFIS method was applied to train and establish the real-time crash risk prediction model. The prediction accuracy of the crash occurrence could reach 65% when 0.60 was considered as the crash prediction threshold.

Alzheimer’s Disease Image Segmentation with Self-Organizing Map Network

Detection and segmentation of Alzheimer's disease (AD) is very important because it provides structural information of abnormal and normal tissues. Functional Magnetic Resonance Imaging (FMRI) is a promising tool for detecting brain image. Alzheimer's disease (AD) FMRI image segmentation is treated as texture classification problem. In this paper proposed a novel method for AD image segmentation using Self-Organizing Map Network (SOMN). Four different types of features are extracted from the FMRI AD images such as the first order gray level parameters, multi-scale features, textural measures and moment invariant features. These features are used for image segmentation. Application of Adaptive Neuro-Fuzzy Inference System (ANFIS) model utilized for classification of Alzheimer's disease segmentation image. The proposed Segmentation and classification results are promising. Key word: Alzheimer's disease (AD), self-organizing map network (SOMN), ANFIS (artificial neural network fuzzy inference system), segmentation.

Assessment of ANFIS networks on wavelet packet levels in generating artificial accelerograms

Based on Adaptive Neural Network Fuzzy Inference System ANFIS networks, this paper presents a novel approach to generate artificial earthquake accelerograms from available data, which are compatible with specified design or response spectra. The proposed procedure uses the learning abilities of ANFIS networks as a powerful tool to develop the knowledge of the inverse mapping from response spectrum to earthquake records. Furthermore, to obtain better simulation results, Wavelet Packet Transform WPT and Principle Component Analysis PCA are used to convert records and response spectra from real to transformed spaces. Then, ANFISs are trained to relate response spectrum of records to their wavelet packet coefficients. In this process, the same results of different training levels of ANFIS method are obtained. In order to clarify the efficiency and accuracy of the proposed method, the results have been compared with the outcomes of previous artificial earthquake accelerograms generation methods. Finally, several interpretive examples are provided to demonstrate success of the suggested method.

Adaptive Neural Network Fuzzy Control Plane Double Inverted Pendulum

Double inverted pendulum system is nonlinear and unstable. Fuzzy control uses some expert's experience knowledge and learns approximate reasoning algorithm. For it does not depend on the mathematical model of controlled object, it has been widely used for years. In practical engineering applications, most systems are nonlinear time-varying parameter systems. As the fuzzy control theory lacks of on-line self-learning and adaptive ability, it can not control the controlled object effectively. In order to compensate for these defects, it introduced adaptive, self-organizing, self-learning functions of neural network algorithm. We called it adaptive neural network fuzzy inference system (ANFIS). ANFIS not only takes advantage of the fuzzy control theory of abstract ability, the nonlinear processing ability, but also makes use of the autonomous learning ability of neural network, the arbitrary function approximation ability. The controller was applied to double inverted pendulum system and the simulation results showed that this method can effectively control the double inverted pendulum system.

Neuro-fuzzy controller to navigate an unmanned vehicle

A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Fuzzy Logic Controller can very well describe the desired system behavior with simple “if-then” relations owing the designer to derive “if-then” rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, an artificial neural network fuzzy inference system (ANFIS) controller is described and implemented to navigate the autonomous vehicle. Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous methods like Artificial Neural Network (ANN).

A two-stage approach based on ANFIS and fuzzy goal programming for supplier selection

In this paper a hybrid approach based on artificial neural network fuzzy inference system (ANFIS) and fuzzy goal programming (FGP) has been proposed to deal with the supplier selection problem. Under lack of thorough knowledge for supplier selection problem in real conditions, knowledge of experts is usually a proper substitute. Proposed approach includes two stages. First, an ANFIS model is constructed to obtain overall utility of suppliers based on the expert’s experiences by considering interrelations between four main criteria obtained through the decision team. At the second stage, an FGP model developed to obtain the optimal purchasing quantity from the suppliers with respect to the goals and model constraints. Finally, the proposed procedure has been applied in a wire harness company in Iran.

Comparison of Different Modeling Concepts for Drying Process of Baker's Yeast

Abstract Abstract This study investigates different modeling approaches and compares for drying of baker's yeast in a fluidized bed dryer. Four modeling concepts were investigated: modeling based on the mass and energy balance, modeling based on diffusion mechanism in the granule, modeling based on recurrent Artificial Neural Network (ANN) and modeling based on Adaptive Neural Network Fuzzy Inference System (ANFIS). Dry matter of product, product temperature and product quality were predicted using these model structures. To evaluate performances of the modeling structures, industrial scale drying process data were used.

Greedy Kernel Components Acting on ANFIS to Predict BOF Steelmaking Endpoint

In order to make overall consideration of the information from the original variables in the basic oxygen furnace (BOF) steelmaking dynamic process, an adaptive neural network fuzzy inference system (ANFIS) model based on kernel and greedy components is proposed. This kind of model can improve the endpoint predicting precision of the steel carbon contents and temperature. After hidden information is exposed in the high feature space through the kernel function transformation, greedy algorithm is used to remove redundant information and reduce the dimensions. The extracted components are used as the new inputs of ANFIS, and the implication relation among the inputs is reflected by rules, which simulate the operators experience, and consequently reduce the influence resulted from different operators. When the practical data are simulated, the simulated results are close to the practical values. The method is effective.