Fuzzy Neural Network Approach Research Articles

A Fuzzy Neural Network Approach to Adaptive Robust Nonsingular Sliding Mode Control for Predefined-Time Tracking of a Quadrotor

A Fuzzy Neural Network Approach to Adaptive Robust Nonsingular Sliding Mode Control for Predefined-Time Tracking of a Quadrotor

Comparative study of real-time photovoltaic fault diagnosis using artificial intelligence: Fuzzy logic and neural network approaches

ABSTRACT Solar photovoltaic (PV) systems are becoming increasingly popular for renewable energy production. However, due to environmental and operational conditions, various faults can occur in PV modules, which can cause a significant reduction in system performance. This study suggests employing two distinct approaches, Fuzzy Logic (FL) and Artificial Neural Networks (ANN), to diagnose defects in photovoltaic (PV) systems. Both the simulation and measuring are used to compare the performance of various strategies. The simulation was performed using MATLAB/Simulink, while the experiment was carried out using the dSPACE DS1104 platform to apply the diagnostic model built in the Matlab/Simulink® program. The suggested approaches have been verified using an experimental database of climatic and electrical attributes obtained from a photovoltaic (PV) panel situated at the LGEB Laboratory of the University of Biskra in Algeria. For six single- and multi-fault types, partial shading, soiling, SC of one by-pass diode, SC of two by-pass diodes, by-pass diode shunted and shading & by-pass diode disconnected. A dSPACE DS1104 platform which shows the results and informs the user about the state of the PV panel has also been developed. The results of the simulation show that both FL and ANN methods can accurately detect and diagnose the six types of faults by 100% and 99.7%, respectively. The study demonstrates the effectiveness of both FL and ANN methods for PV fault diagnosis. However, in the experimental tests, the ANN method shows better performance in terms of accuracy compared to FL by 99.6% and 99.2%, respectively, and speed takes only 1.04s, while FL consumes 3.02s and could be a more practical solution for real-time surveillance PV fault diagnosis

Prediction of inlet-to-outlet width ratio of type-A piano key weir using fuzzy neural network (FNN)

ABSTRACT A Piano Key Weir (PKW) is a nonlinear (labyrinth-type) weir with a small spillway footprint and a large discharge carrying capacity. It (PKW) enables water bodies to continue functioning at elevated supply levels while causing no damage to dam structures, resulting in increased storage. PKW's geometrical structure is extremely complex, and geometrical aspects have a significant impact on its efficiency and on energy dissipation. Among them relative width ratio (Wi/Wo) (i.e., inlet to outlet key width ratio) is a critical parameter that affects the PKW's discharge efficiency, and energy dissipation across the weir significantly. This study predicts the PKW's inlet to the outlet key ratio and understands the resulting hydraulic behaviours based on a Fuzzy Neural Network (FNN). The dataset used in this study was collected experimentally, which adds to the study's authenticity because it is not a conventional dataset. The model's performance is evaluated by the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE); both values are 0.0305 and 0.0222, respectively. According to the dataset, these scores tell the model's reliability as it is in the ideal range. The FNN approach can be applied in a variety of fields to predict or solve different problems erent problems.

A Fuzzy Neural Network Approach for Evaluation of Wetland Restoration Programmes

A Fuzzy Neural Network Approach for Evaluation of Wetland Restoration Programmes

Modified Fuzzy Neural Network Approach for Academic Performance Prediction of Students in Early Childhood Education

Modern education relies heavily on educational technology, which provides students with unique learning opportunities and enhances their ability to learn. For many years now, computers and other technological tools have been an integral part of education. However, compared to other educational levels, the incorporation of educational technology in early childhood education is a more recent trend. It is because of this that materials and procedures tailored to young children must be created, implemented, and studied. The use of artificial intelligence techniques in educational technology resources has resulted in better engagement for students. Early childhood special education students' academic achievement is predicted using a Modified Fuzzy Neural Network (MFNN). Before constructing the classifier, the dataset had to be preprocessed to remove any extraneous information. As a follow-up, this study will put to the test an organized approach to the implementation of customized fuzzy neural networks for the prediction of academic achievement in early childhood settings. Considerations for the analysis of academic achievement in early childhood education are discussed in this article, including recommendations for the implementation of proposed modified fuzzy neural networks. In terms of evaluation metrics such as Precision, recall, accuracy, and the F1 coefficient, the proposed model outperforms conventional machine-learning (ML) techniques.

Optimizing teaching management in college physical education: a fuzzy neural network approach

Optimizing teaching management in college physical education: a fuzzy neural network approach

Circular economy of medical waste: novel intelligent medical waste management framework based on extension linear Diophantine fuzzy FDOSM and neural network approach.

Environmental pollution has been a major concern for researchers and policymakers. A number of studies have been conducted to enquire the causes of environmental pollution which suggested numerous policies and techniques as remedial measures. One such major source of environmental pollution, as reported by previous studies, has been the garbage resulting from disposed hospital wastes. The recent outbreak of the COVID-19 pandemic has resulted into mass generation of medical waste which seems to have further deteriorated the issue of environmental pollution. This necessitates active attention from both the researchers and policymakers for effective management of medical waste to prevent the harm to environment and human health. The issue of medical waste management is more important for countries lacking sophisticated medical infrastructure. Accordingly, the purpose of this study is to propose a novel application for identification and classification of 10 hospitals in Iraq which generated more medical waste during the COVID-19 pandemic than others in order to address the issue more effectively. We used the Multi-Criteria Decision Making (MCDM) method to this end. We integrated MCDM with other techniques including the Analytic Hierarchy Process (AHP), linear Diophantine fuzzy set decision by opinion score method (LDFN-FDOSM), and Artificial Neural Network (ANN) analysis to generate more robust results. We classified medical waste into five categories, i.e., general waste, sharp waste, pharmaceutical waste, infectious waste, and pathological waste. We consulted 313 experts to help in identifying the best and the worst medical waste management technique within the perspectives of circular economy using the neural network approach. The findings revealed that incineration technique, microwave technique, pyrolysis technique, autoclave chemical technique, vaporized hydrogen peroxide, dry heat, ozone, and ultraviolet light were the most effective methods to dispose of medical waste during the pandemic. Additionally, ozone was identified as the most suitable technique among all to serve the purpose of circular economy of medical waste. We conclude by discussing the practical implications to guide governments and policy makers to benefit from the circular economy of medical waste to turn pollutant hospitals into sustainable ones.

Neutrosophic Fuzzy Neural Network Modelling and Current-Voltage Analysis for Forecasting Post-Surgery Risks

The electrical reaction of bioactive sites in the individual’s body can be used to diagnose various disorders. Forecasts are made by examining the electric signal of the biologically active points onto patients. Measurements of the organ’s present level and variations in the passive electrical characteristics at specific bioactive sites on the body were made to evaluate the influence on the organ. The study aims to create a Neutrosophic fuzzy neural network (NFNN) approach to forecast the probability of complications following surgery. The research investigates a neural network method for predicting hazards associated with post-surgical care. Examining the current-voltage features of the biologically active spots forms the basis for the characteristics of the risk classifiers. By looking at patients who had been given a diagnosis of a disease, the training, as well as verification samples, as well as verification samples were created. Patients with type 1 had successful operations, but type 2 patients experienced a variety of post-operative problems, and type 3 patients needed extra treatment. The created classifiers show an excellent ability to foresee severe circumstances during surgical therapy. The neutrosophic fuzzy neural network model may be more sophisticated and advanced compared to conventional fuzzy neural network models. It can help differentiate the proposed model from existing models and highlight its unique features and advantages. The results show that the proposed

Flow direction algorithm and Biogeography-based optimization via fuzzy neural network approach for the prediction of high-performance concrete strength property

Concrete is one of the most useful materials in the construction industry. Conventional concrete comprises additives such as cement, water, and aggregates. This concrete cannot be used for very important and sensitive structures. For this reason, high-performance concrete (HPC) has been used to achieve the desired and more suitable compressive strength by employing some additives. The additional variable is cement, fly ash, blast furnace slag, superplasticizer, fine aggregate, and coarse aggregate. On the other hand, to obtain a mixture of these materials, laboratory work is not economical and saves time. Therefore, soft-based modeling is the order of the day to solve this problem. The adaptive network fuzzy inference system model is one of the ways to achieve compressive strength close to the laboratory model, which is a smart modeling move. This model has to be optimized to get better and more satisfying results, which is done by two optimizers, Biogeography-Based Optimization (BBO) and Flow Direction Algorithm (FDA), which have bright created and powerful for better performance. Furthermore, in the outputs of these two models, BBO-ANFIS and FDA-ANFIS, certain errors and desired percentages are used to select the most suitable and ideal model for the desired output, i.e., the compressibility of concrete in high-performance concrete. In the relevant modeling, the number of evaluators in the ANFISBBO combined model is R2 = 0.8926, RMSE = 5.0406, MAE = 3.7145, A20-index = 0.8382 and U95 = 13.881, and in ANFISFDA, R2 = 0.912, RMSE = 4.7294, MAE = 3.5367, A20-index = 0.8414, and U95 = 13.054 is obtained. According to the obtained numbers, it is clear that the ANFISFDA combined model has been able to get better results than the BBO-ANFIS model.

Development and Utilization of English Online Course Resources Based on Fuzzy Neural Network

Since the reform and development, after decades of changes in the times, China’s basic education reform has been promoted several times, and the degree of information technology in the campus will become one of the important factors for parents to choose this school or not. From digital projectors since 2003 to multimedia-assisted English teaching nowadays, it is evident that online English teaching has become an important part of today’s English online resources, but most of the English online resources nowadays are developed by professional research institutions, which leads to a situation where online English curriculum resources are not closely connected to the actual English teaching mode, and more importantly, there is a lack of online English course resources and they are outdated. In the new curriculum reform requirements, the development and utilization of English online resources are clearly pointed out, which shows the importance of developing and utilizing English online course resources and the need to develop in the direction of innovation and modernity, and the need to consider combining the development and utilization of English online course resources with modern technological progress, not only at the theoretical level but also at the practical level. English is a comprehensive and practical language that needs to be used and reinforced by users in order for them to be able to communicate and express their thoughts in English. In this paper, we adopt a fuzzy neural network approach and a gradient descent algorithm to address the shortcomings of current English online course resource development and utilization, and adopt an averaging process for weight updates, and then realize the control of the number of fuzzy rules by adding pooling operations. The experimental results show that this paper can effectively develop English online resources.

Design of fuzzy logic based energy management and traffic predictive model for cyber physical systems

Cyber-Physical Systems (CPS) is a multidisciplinary effort that links cyber and physical vectors. Cyber systems may be incorporated into any physical system, such as intelligent transportation systems (ITS), industries, and cities, to improve its intelligence, energy efficiency, and comfort. The design of self-driving cars has a large influence on vehicle travel demand and energy consumption. ITS also heavily relies on the Traffic Flow Prediction (TFP) system. With this rationale, this work focuses on the development of a Fuzzy Logic-based Energy Management and TFP model (FLEM-TFP) for CPS in ITS. The FLEM-TFP approach proposed here involves two primary processes: energy management and TFP. An adaptive neuro-fuzzy inference system (ANFIS) model is also utilized to compute the engine torque required based on various readings. In addition, a sailfish optimizer (SFO)-based fuzzy wavelet neural network (FWNN) approach is used in the ITS to estimate traffic flow. According to the findings of the experiments, the FLEM-TFP approach outperformed previous state-of-the-art procedures.

A survey on cooperative control strategies for DC microgrids

Distributed generators have been widely used in modern power systems, and microgrids are becoming a key concept for the integration of multiple distributed generators and clusters of loads with great efficiency and high system reliability. In a microgrid, the control strategy is one of the most important issues. Various control schemes have been proposed to achieve the cooperation of distributed generators in a microgrid. This paper surveys cooperative control strategies for multiple distributed generators in DC microgrids. The three popular coordinated control strategies are presented: decentralized control, centralized control, and distributed control. The advantages and drawbacks of the control strategies are compared. Some intelligent control methods such as fuzzy logic control and neural network approaches are also briefly introduced in the context of microgrids. Further research trends and challenges in this area are also discussed.

A trust-based fuzzy neural network for smart data fusion in internet of things

Internet of Things (IoT) devices generates a vast amount of data from extensive applications. Maintaining the sensed data with low energy consumption, delay time, and adaptive coverage fraction rate proportionally influences the storage capacity. To maintain a trade-off between above-listed factors, we proposed an Elfes Sugeno Fuzzy and Trust-based Neural Networks (ESF-TNN) approach enables 3-algorithms. First, Elfes Probability Sensing (EPS) Model addresses the coverage fraction of each IoT sensor. Second, Sugeno Fuzzy Processing model regulates the energy consumption by proportionately distributing data to nodes without the defuzzification process. Third, Trust-based Neural Data Storage algorithm enriches an adequate data storage capacity by considering the average classification ratio while processing regenerated data packets to pertain each interaction information via Trust Mechanism. Simulation results show that our proposed method effectively covers the monitored area with 15 Joules of energy consumption and 1-ms delay time along with sufficient storage capacity.

Observer‐based controller design for uncertain disturbed Takagi‐Sugeno fuzzy systems: A fuzzy wavelet neural network approach

SummaryIn this article, we develop a novel method to design a controller for nonlinear systems represented by Takagi‐Sugeno (T‐S) fuzzy model in the presence of unknown dynamics, uncertainties in parameters of nonlinear system and external disturbances. The control law is constituted two segments. The first segment derives from parallel distributed compensation (PDC) procedure, in which each control rule is drawn from the respective rule of T‐S fuzzy model. The second segment stems from fuzzy wavelet neural network (FWNN) estimator which is evoked by the hypothesis of multiresolution analysis (MRA) of wavelet transforms and fuzzy notions, so as to approximate the uncertainties and external disturbances in T‐S fuzzy model accurately. In this regard, the Lyapunov stability theorem is applied to acquire the adaptive learning laws for training and tuning online FWNN parameters such as the dilations, translations, and the weights of networks. Moreover, the asymptotic stability of the closed loop system is guaranteed based on the Lyapunov stability theorem. Furthermore, a development of the proposed method to observer‐based controller design for uncertain nonlinear systems descripted by T‐S fuzzy model is provided. The efficiency and robustness of the proposed method are illustrated by simulation outcomes. It is noteworthy that the proposed controller remarkably handles the uncertainties and external disturbances in T‐S fuzzy model without employing traditional conservative lemma and without considering bounds on uncertainties.

A Situation Assessment Method with an Improved Fuzzy Deep Neural Network for Multiple UAVs

To improve the intelligence and accuracy of the Situation Assessment (SA) in complex scenes, this work develops an improved fuzzy deep neural network approach to the situation assessment for multiple Unmanned Aerial Vehicle(UAV)s. Firstly, this work normalizes the scene data based on time series and use the normalized data as the input for an improved fuzzy deep neural network. Secondly, adaptive momentum and Elastic SGD (Elastic Stochastic Gradient Descent) are introduced into the training process of the neural network, to improve the learning performance. Lastly, in the real-time situation assessment task for multiple UAVs, conventional methods often bring inaccurate results for the situation assessment because these methods don’t consider the fuzziness of task situations. This work uses an improved fuzzy deep neural network to calculate the results of situation assessment and normalizes these results. Then, the degree of trust of the current result, relative to each situation label, is calculated with the normalized results using fuzzy logic. Simulation results show that the proposed method outperforms competitors.

An Initial Framework of Fuzzy Neural Network Approach for Online Learner Verification Process

An Initial Framework of Fuzzy Neural Network Approach for Online Learner Verification Process

Path-tracking of autonomous vehicles using a novel adaptive robust exponential-like-sliding-mode fuzzy type-2 neural network controller

Abstract Structured and unstructured uncertainties together with external disturbances may pose considerable challenges in realizing desired path-tacking and lane keeping of autonomous vehicles. This paper presents a novel robust adaptive indirect control method based on an exponential-like-sliding-mode fuzzy type-2 neural network approach for enhanced path-tracking performance of road autonomous vehicles subject to parametric uncertainties related to vehicle nominal cornering stiffness, road-tire adhesion coefficient, inertial parameters, and forward speed. A hierarchical controller is designed and the stability of the closed-loop system is ensured along with deriving the adaptation laws by employing the Lyapunov stability theorem. The conventional reaching law related to the sliding mode degrades from the system stability and introduces an inherent chattering of the controller input. The convergence law for the sliding surface is adjusted based on a variable exponential sliding manifold to eliminate possible chattering in the buffeting switch zone near the origin. The proposed exponential-like sliding surface guarantees the swift and smooth global asymptotic convergence of the tracking error toward zero. Furthermore, an adaptive look-ahead path-tracking error term is introduced as an auxiliary error criterion to improve the vehicle path-tracking performance. The effectiveness of the proposed controller is verified through Matlab/Simulink–CarSim co-simulations and comparisons with selected reported control methods for two different road maneuvers. The results suggested substantially improved tracking performance by the proposed controller with robustness to withstand against the perturbed parameters and external disturbances.

Log Data Analysis with ANFIS: A Fuzzy Neural Network Approach

This study explores the use of the Adaptive Neuro-Fuzzy Inference System (ANFIS), a neuro-fuzzy approach, to analyze the log data of technology-based assessments to extract relevant features of student problem-solving processes, and develop and refine a set of fuzzy logic rules that could be used to interpret student performance. The log data that record student response processes while solving a science simulation task were analyzed with ANFIS. Results indicate the ANFIS analysis could generate and refine a set of fuzzy rules that shed lights on the process of how students solve the simulation task. We conclude the article by discussing the advantages of combining human judgments with the learning capacity of ANFIS for log data analysis and outlining the limitations of the current study and areas of future research.

Integrating multiple methods to enhance medical data classification

In medical data classification, data reduction and improving classification performance are the important issues in the current scenario. In existing medical data classification methods, initially, the medical data pre-processing is performed. After pre-processing feature selection is performed, otherwise, the process is more time consuming and has poor accuracy. Here we have proposed two algorithms for enhancing the classification performance on medical data. In first proposed method Bag of Words technique is used for better feature subset selection. Subsequently, the hybrid Fuzzy-Neural Network approach used that can handle imprecision in data while classification. This combination of feature selection technique and Fuzzy-Neural Network classifier approach gives enhanced classification accuracy. In the second proposed algorithm, we have integrated data cleaning technique to improve data quality as pre-processing technique along with bag of words and Fuzzy-Neural Network, this method performs classification on clean filtered data with appropriately reduced feature set that results in more accurate classification than the existing methods. Thus in proposed approaches we have tried to handle three issues, removing noise in data, optimal feature subset selection and handling imprecision in data. The comparative study of various medical datasets in terms of accuracy shows that the two proposed algorithms perform better as compared to existing techniques and the enhancement obtained is around 3% and 17% respectively. In addition the performance of Bag of Words feature selection method used in the proposed system is compared with two feature selection methods LSFS and SFFS.

A comparison between knowledge-driven fuzzy and data-driven artificial neural network approaches for prospecting porphyry Cu mineralization; a case study of Shahr-e-Babak area, Kerman Province, SE Iran

The study area, located in the southern section of the Central Iranian volcano–sedimentary complex, contains a large number of mineral deposits and occurrences which is currently facing a shortage of resources. Therefore, the prospecting potential areas in the deeper and peripheral spaces has become a high priority in this region. Different direct and indirect methods try to predict promising areas for future explorations, most of which are very time-consuming and costly. The main goal of mineral prospecting is applying a transparent and robust approach for identifying high potential areas to be explored further in the future. This work presents the procedure taken to create two different Cu-mineralization prospectivity maps. The first map is created using a knowledge-driven fuzzy technique and the second one by a data-driven Artificial Neural Network (ANN) approach. In this study aim is to investigate the results of applying the ANN technique and to compare them with the outputs of applying the fuzzy logic method. The geo-datasets employed for creating evidential maps of porphyry Cu mineralization include the solid geology map, alteration map, faults, dykes, airborne total magnetic intensity, airborne gamma-ray spectrometry data (U, Th, K and total count), and known Cu occurrences. Based on this study, the ANN technique is a better predictor of Cu mineralization compared to the fuzzy logic method. The ANN technique, due to capabilities such as classification, pattern matching, optimization, and prediction, is useful in identifying the anomalies associated with the Cu mineralization.