Fuzzy Rules Research Articles

A New Adaptive Strategy for Enhancing the Stability of Isolated Grids through the Integration of Renewable Energy and V2G Management

The integration of renewable energy sources into isolated microgrids introduces significant power fluctuations due to their intermittent nature. This study addresses the need for advanced power smoothing methods to enhance the stability of isolated networks. An innovative adaptive strategy is presented, combining photovoltaic solar generation with vehicle-to-grid technology, utilizing an enhanced adaptive moving average filter with fuzzy logic control. The primary objective is to dynamically optimize the time frame of the Li-ion battery energy storage system for immediate power stabilization, leveraging the high energy density and rapid response capabilities inherent in electric vehicle batteries. The methodology encompasses data acquisition from photovoltaic panels, definition of fuzzy logic control rules, and implementation of the proposed method within a computer-controlled system connected to a bidirectional three-phase inverter. Experimental results highlight the proposed method’s superiority over conventional moving averages and ramp-rate filters.

Vibration control and robustness analysis of tensegrity structures via fuzzy dynamic sliding mode control method

This study addresses the problem of mitigating vibration responses in tensegrity structures subjected to complex dynamic loading conditions. The overall purpose is to develop an effective control method to enhance the stability and performance of these structures. To achieve this, a fuzzy dynamic sliding mode control (FDSMC) method is proposed. The methodology involves deriving the state space expression of the controlled system based on the tensegrity structure's dynamic model, followed by designing a dynamic sliding mode controller using the reaching law. Fuzzy rules are incorporated to adaptively adjust the dynamic sliding mode parameters, accounting for uncertainties in controller parameters, thereby ensuring high efficiency and smooth controller output. A comparative analysis scheme, focusing on identical energy input on actuators, is utilized to evaluate the performance of various active vibration control methods. Two illustrative examples, a spatial double-layer tensegrity beam and a complex tensegrity spiral tower, are investigated in detail. The findings demonstrate that the FDSMC method significantly reduces structural dynamic responses and improves algorithm robustness compared to the conventional linear quadratic regulator (LQR) method, indicating its promising potential for relevant engineering applications.

Implementing fuzzy SMRGT, ANN, and ANFIS for flow coefficient estimation in Antalya River Basin

The estimation of the flow coefficient is a vital hydrological procedure that holds considerable importance in flood prediction, water resource management, and flood mitigation. The precise estimation of the flow coefficient is imperative in mitigating flood-related damages, administering flood alert mechanisms, and regulating water discharge. It is hard to accurately determine the flow coefficient without a good understanding of the river basin’s hydrology, climate, topography, and soil characteristics. A range of methodologies have been documented in the most recent body of literaturefor flow coefficient modeling. The majority of these methods, however, depend on opaque techniques that lack generalizability. Therefore, this research employed three distinct methodologies—specifically, the Adaptive Neural Fuzzy Inference System (ANFIS), the Simple Membership Function, and the Fuzzy Rules Generation Technique (SMRGT) are all examples of fuzzy inference systems, and Artificial Neural Network (ANN), to achieve its objectives. The Aksu River Basin in Antalya, Turkey, was chosen as the study area. The models underwent multiple permutations of precipitation (P), temperature (T), relative humidity (Rh), wind speed (Ws), land use (LU), and soil properties (Sp) data that were tailored to the particular study region. The study analyzed the results using various performance metrics of the model such as mean absolute error (MAE), Nash–Sutcliffe efficiency coefficient (NSE), root mean square error (RMSE), and correlation coefficient (R2). The results indicate that the SMRGT method resulted in a remarkable degree of accuracy in forecasting the flow coefficient, as demonstrated with the minimal RMSE and MAE values and high correlation coefficient values. The study’s findings suggest that the SMRGT method was applied effectively in hydrological analysis to estimate the flow coefficient, contributing to more accurate flood prediction, water resource management, and flood mitigation strategies.

Genetic fuzzy rules and hybrid QDCNN-F-DSAE for detecting attacker behavior with tuning of firewall

ABSTRACT The application of computer technology is increasing quickly as the field of information technology develops over time. Because of the growth of computer networks, there is a rise in network attacks. Moreover, the majority of transactions are conducted via the Internet, and then the web application security is becoming increasingly important. A network firewall shields the web servers from unauthorised and dangerous traffic. In this work, the hybrid Quantum Dilated Convolutional Neural Network fused Deep-Stacked Auto Encoder (QDCNN-F-DSAE) model with Genetic Fuzzy System (GFS) is developed for network protection. With the utilisation of genetic fuzzy system (GFS) and firewall tuning, the QDCNN-F-DSAE identified the attack, ransomware activity, and prompt decision-making. The data offered by the simulated environment is used to make decisions, and the decisions should be made quickly within a few seconds using the proposed QDCNN-F-DSAE with GFS. Furthermore, it identifies the attacks and alerts the users to store the data from the supplied source. The malicious function and protected private Local Area Network (LAN) are discovered and removed by the firewall after the formation of genetic fuzzy rules. Moreover, this model is estimated using the accuracy, sensitivity, and specificity measures that offered the finest values of 0.915, 0.908, and 0.920.

Asian rust of soybean favorability Mato Grosso-Brazil

This study presents the map that indicates favorable environmental conditions for the development of Asian soybean rust in Mato Grosso, for a given period. It’s about a plant's disease caused by the fungus Phakopsora pachyrhizi, which has soybean as its main host. By affecting the plantation, anticipates the ripening of the grains, which can considerably reduce your productivity. The map is the reproduction, for Mato Grosso, of the image developed by Alves et al. (2011) for the state of Minas Gerais, generated from a fuzzy logic-based rule system. The input variables for this system are temperature and leaf wetness duration, while the output is the area below the disease progression curve. To supply it, data were collected from automatic meteorological stations located in the state and neighboring regions. Records were made available hourly for January 2019. In possession of these results, for interpolation were tested the inverse distance weighting and kriging models. The method that showed the best results was the first, with an exponent equal to two.

A fuzzy coefficient deep flux weakening algorithm for IPMSM without out-of-control

A fuzzy coefficient q-axis current increment flux weakening (FCCIFW) control method is proposed in interior permanent magnet synchronous motor (IPMSM) drives. The proposed FCCIFW not only simplifies the calculation of flux weakening coefficient according to the derived formula, but also avoids the saturation of current regulator. In FCCIFW, the theoretical calculation formula of conversion coefficient is derived firstly, and then the fuzzy rule base is established according to the formula. FCCIFW not only refrains the use of complex flux weakening calculation formula or fixed control parameters, but also refrains the problem of out-of-control in the process of deep flux weakening control, so as to improve the overall performance in the process of flux weakening. The results show that the proposed method has an effective and correct calculation process and results, Compared with different control methods in the operation process, it shows that the proposed method has more stable control effect, which has great application value in engineering.

Fuzzy Clustering Algorithm for Trend Prediction of The Digital Currency Market

Digital currencies, such as Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Stellar (XLM) and Tether (USDT), have been attracting the interest of investors and speculators. Over the last several years, the exponential growth in the value of digital currency has captured the interest of many individuals who see it as an attractive investment opportunity. After all, investors must deal with the expected volatility of Bitcoin prices as part of their investments. The future development of cryptocurrency can be challenging to forecast because of the extreme unpredictability and disorder of external events. In this research, fuzzy models for cryptocurrency price forecasting using a level set-based Fuzzy Clustering Based on Multi-Criteria Decision-Making (FC-MCDM). Compared to linguistic and functional fuzzy clustering, the construction and processing of fuzzy rules in a multi-criteria decision-making-based collection set differ. Based on level sets, the model produces the weighted average of the functions that active fuzzy rules provide as output. In the model's outputs, the activation levels of the fuzzy rules are represented directly by the output functions. Computational experiments are carried out to test the efficacy of the level-set approach for one-step-ahead prediction of cryptocurrency closing prices. Meanwhile, level set-based fuzzy clustering outperforms the other methods when the direction of price change evaluates performance

Simulation and analysis of the electro-hydraulic coordinated system for the disc brake system in mining belt conveyor

The present study aims to investigate the dynamic performance of the disk hydraulic brake system in a mining belt conveyor. To achieve this, a simulation model of the hydraulic system for the belt conveyor is designed and constructed using AMESim. It is recognized that brake disk deformation can lead to brake shock, thus an ADAMS rigid-flexible coupling model for hydraulic disk brakes is established. Furthermore, the concept of rigid-flexible coupling mechanical systems is introduced into the hydraulic system to enable interaction between physical quantities. Use fuzzy logic rules to adjust parameters in Simulink, and in response to the suboptimal control of braking and acceleration, a compact non-model-based adaptive algorithm is incorporated into the conventional PID control algorithm. Employing mechanical modeling and multidisciplinary dynamic collaborative simulation technology, a simulation system integrating electromechanical and hydraulic aspects is developed for the disk hydraulic brake of belt conveyors. This system is employed to conduct simulation studies on the closed-loop control of braking disk speed and deceleration/acceleration for belt conveyors. The study findings reveal that the non-model-based adaptive PID control algorithm, in comparison to fuzzy PID, not only demonstrates quicker and more stable responses across various dynamic performance curves but also markedly reduces the axial oscillation amplitude of the braking disk under braking conditions.

Adaptive extreme learning machine using soft computing fuzzy propositions—Validating operating state of solar energy system

In this paper soft computing fuzzy proposition/rule based adaptive extreme learning is proposed. This article illustrates the impact of agricultural solar panel (AgSP) and its operating condition/state during the farming seasons and hence safety of energy system sustained. Extreme learning machine (ExLM) used as learning machine (LM) for artificial intelligence (AI) training to predict the operating state of applied AgSP green energy unit. soft computing Fuzzy rules are framed for every instant of leaning machine to identifying the lower mismatch conditions and avoid the false prediction. Higher and lower limits of adaptive resonance theory (ART) support LM (ART-LM) is properly used by providing unusual dust formation (UnDFR) data and usual/normal dust formation (UsDFR) data for AI training. Outcome of AI neural net is determined by resonant neuron net (RSNN). Predicted variable from ART-LM is calculated with higher and lower limits of dataset captured from applied energy common node (CN). Many test conditions of dust formation on solar photovoltaic (PV) has been validated to predict the state of applied energy system. For any UnDFR confirmation, entire AgSP based distributed generator (DG) unit will isolate from common node/utility grid. Proposed ART-LM based AI algorithm has been validated even for charging state events of electrical vehicle (EV) during the abnormal conditions.

Evaluation of Total Risk-Weighted Assets in Islamic Banking through Fintech Innovations

The assessment of total risk-weighted assets (LTRWAs) in the banking sector is of the utmost importance. It serves as a critical component for regulatory compliance, risk management, and capital adequacy. By accurately assessing LTRWAs, banks can effectively meet regulatory requirements, efficiently allocate capital resources, and proactively manage risks. Moreover, the accurate assessment of LTRWAs supports performance evaluation and fosters investor confidence in the financial stability of banks. This study presents statistical analyses and machine learning methods to identify factors influencing LTRWAs. Data from Bahrain, Jordan, Qatar, the United Arab Emirates, and Yemen, spanning from 2010 to 2021, was utilized. Various statistical tests and models, including ordinary least squares, fixed effect, random effect, correlation, variance inflation factor, tolerance tests, and fintech models, were conducted. The results indicated significant impacts of the unemployment rate, inflation rate, natural logarithm of the loan-to-asset ratio, and natural logarithm of total assets on LTRWAs in regression models. The dataset was divided into a training group (90% of the data) and a testing group (10% of the data) to evaluate the predictive capabilities of various fintech models, including an adaptive network-based fuzzy inference system (ANFIS), a hybrid neural fuzzy inference system (HyFIS), a fuzzy system with the heuristic gradient descent (FS.HGD), and fuzzy inference rules with the descent method (FIR.DM) models. The selection of the optimal model is contingent upon assessing its performance according to specific error criteria. The HyFIS model outperformed others with lower errors in predicting LTRWAs. Independent t-tests confirmed statistically significant differences between original and predicted LTRWA for all models, with HyFIS showing closer predictions. This study provides valuable insights into LTRWA prediction using advanced statistical and machine learning techniques, based on a dataset from multiple countries and years.

Development of a MIMO fuzzy inference system—PI controller for a closed‐circuit grinding ball mill circuit

AbstractThis article aims to study the implementation of classical proportional‐integrative (PI) controllers and their coupling with the fuzzy inference systems (FISs) in the act of closed‐circuit grinding (CCG) ball mill system. The system was formed for a multiple‐input multiple‐output (MIMO) system, with two inputs, the feed rate (WF) and speed classifier rotor (VR), and two outputs, a sieve fraction 45 μm (P45) and the amount of material by a weight inside the drum (hold up [HU]). The model was simulated based on experimental processes and control strategies. The fuzzy‐PI controllers were developed on the software, and the data from this process were used to build the database and the necessary knowledge to construct the FIS controllers (with fuzzy rules base 3 × 3 and 5 × 5). Their implementation decreases the error criteria integral of time multiplied by the absolute error (ITAE) and integral of the absolute magnitude of the error (IAE) by 35% and 65%, respectively. Although, applying fuzzy‐PI systems with a smaller rule‐based outcome gives the benefits of implementing the fuzzy logic (FL) but with a smaller oscillatory performance and a minor negative effect on HU control.

Fuzzy Rules in Knowledge Based Diagnosis of Anthracnose Disease in Grape Crop

Fuzzy Rules in Knowledge Based Diagnosis of Anthracnose Disease in Grape Crop

OSSEFS: An online semi-supervised ensemble fuzzy system for data streams learning with missing values

The incompleteness of data samples in data streams always affects the performance of learning model. In order to learn data streams with missing values from features and a large amount of unlabeled data, this paper proposes a semi-supervised ensemble algorithm based on evolving fuzzy systems (EFS) named OSSEFS. Aiming at missing values, a fuzzy rule based online imputation strategy is designed to improve the data feature integrity. Aiming at unlabeled data, a new ensemble fuzzy system architecture is proposed to fully utilize both labeled and unlabeled data implicit information to predict data labels accurately. In addition, an online rule parameters optimization strategy is proposed to achieve adaptive adjustment of rule distribution. Thanks to the above strategies, OSSEFS can perform well in data environments containing missing values and unlabeled data. Comparative experimental results on multiple datasets show that OSSEFS outperforms the classical semi-supervised regression algorithms.

An effective metaheuristic based optimized type‐II fuzzy inference system for fault node identification in wireless sensor network

SummaryDeploying a wireless sensor network (WSN) for accessing the network of distant environments is more crucial. Node defect detection is a core technology of WSN and is essential for most WSN networks. The defective node reduces the overall service quality (OSQ) of the global WSN network system. Therefore, a type II fuzzy inference system (T2_FIS) is proposed for detecting defective nodes in the WSN network. The adaptive genetic algorithm (AGA) and proposed method are introduced for tuning the parameters in the T2_FIS system. The proposed T2_FIS method effectively detects the broken node in the WSN network using the fuzzy rules. In addition, the improved Mud Ring (IMR) optimization algorithm is proposed to replace the faulty node with the neighborhood node in the network. The defective nodes are identified and replaced with the closest nodes based on the membership function (MF) generated by the T2_FIS. Furthermore, the lifetime and throughput of the WSN network are increased by minimizing energy consumption. The overall performance is evaluated using the MATLAB tool, and the implementation results are compared with the existing methods. The total energy consumption for the proposed method is 50.451, with a throughput and lifetime of 153.657 and 22979.25. Furthermore, the performance metrics for the existing and proposed strategies are analyzed, and the accuracy of the proposed method is proven to be 99.3827%, the false alarm rate (FAR) is 0.0008, and the false positive rate (FPR) is 0.0617. The results show the effectiveness and performance of the proposed method.

A fuzzy rule extraction method based on Dempster–Shafer theory

A fuzzy rule extraction method based on Dempster–Shafer theory

Research on the characteristics of electro-hydraulic position servo system of RBF neural network under fuzzy rules

A radial basis function neural network PID controller under fuzzy rules (FUZZY-RBF-PID) was designed for the electro-hydraulic position servo system under the influence of uncertain factors such as load mutation, and load stiffness change. Firstly, the mathematical model of the system is established, and the frequency domain and time domain analysis of the system are carried out. Secondly, based on the analysis results, a radial basis function (RBF) neural network PID controller is designed, and fuzzy rules are innovatively used to adjust the learning rate of PID parameters in the RBF neural network learning algorithm in real time. Thirdly, the simulation results show that under the action of the FUZZY-RBF-PID controller, the unit step response of the system has high steady-state accuracy, fast response speed, and under the condition of large load stiffness, the system can recover to the steady-state value faster after being disturbed. At the same time, when the input signal is the sinusoidal signal of 10 HZ, the system under the action of the FUZZY-RBF-PID controller has no obvious phase lag phenomenon, and the tracking error is minimal. The proposed method can effectively improve the comprehensive performance of the electro-hydraulic position servo system under the influence of uncertain factors.

FDiff-Fusion: Denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation

In recent years, the denoising diffusion model has achieved remarkable success in image segmentation modeling. With its powerful nonlinear modeling capabilities and superior generalization performance, denoising diffusion models have gradually been applied to medical image segmentation tasks, bringing new perspectives and methods to this field. However, existing methods overlook the uncertainty of segmentation boundaries and the fuzziness of regions, resulting in the instability and inaccuracy of the segmentation results. To solve this problem, a denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation (FDiff-Fusion) is proposed in this paper. By integrating the denoising diffusion model into the classical U-Net network, this model can effectively extract rich semantic information from input medical images, thus providing excellent pixel-level representation for medical image segmentation. In this paper, a fuzzy learning module is designed on the skip path of U-Net network because of the widespread boundary uncertainty and region blurring of medical image segmentation. The module sets several fuzzy membership functions for the input encoded features to describe the similarity degree between the feature points, and applies fuzzy rules to the fuzzy membership functions, thus enhancing the modeling ability of the model for uncertain boundaries and fuzzy regions. In addition, in order to improve the accuracy and robustness of the model segmentation results, we introduced an iterative attention feature fusion method in the test phase, which added local context information to the global context information in the attention module to fuse the prediction results of each denoising time step. Finally, to validate the effectiveness of FDiff-Fusion, we compare it with existing advanced segmentation networks on the BRATS 2020 brain tumor dataset and the BTCV abdominal multi-organ dataset. The results show that FDiff-Fusion significantly improves the Dice scores and HD95 distance on these two datasets, demonstrating its superiority in medical image segmentation tasks.

Fuzzy rule–based control of multireservoir operation system for flood and drought mitigation in the Upper Mun River Basin

Strategic reservoir operation, a primary water management measures, plays a significant role in mitigating floods and droughts. Since the reservoir operation involves making complicated decisions on uncertain hydrological variables driven by climate variability, therefore, constructive tool for decision making like fuzzy logic is essential to optimize reservoir management and ensure water security. This study demonstrated fuzzy logic application to multiple reservoir operation in tropical region like Thailand. A Fuzzy Rule–Based Model (FRBM) exploiting FL approach was developed to control the upstream reservoir operation in the Upper Mun River Basin (UMRB) using the data from 2008 to 2021. Implementing FRBM for UMRB was conducted by identifying two key variables; available water storage and 7–day ahead predicted inflow, as fuzzy inputs. The fuzzy output of the system is the release fraction determined by three operational condition modules; flood, neutral, and drought. For flood module, fuzzy release is primarily determined by the predicted inflow. However, the determination of reservoir release for drought and neutral modules is influenced by the targeted water demand. The results of base case illustrate the capability of FRBM in increasing reservoir storages at the start of dry season by 123.56 MCM/yr in UMRB due to the new daily release schemes generated. This allows supplying water closer to the theoretical agricultural needs and gross irrigation water requirement potentially reducing the risk of water shortfall during consecutive dry years. Whereas, the maximum fuzzy release is constrained corresponding to safe channel capacity of tributaries and Upper Mun river, therefore, downstream flooding is accordingly prevented.

Implementation of Tsukamoto Fuzzy Logic to Determine Sheep Livestock Population Based on Gender and Age Category in Aceh Tamiang Regency

This study implements the Tsukamoto Fuzzy Logic method to determine the sheep livestock population based on gender and age categories in Aceh Tamiang Regency. The population data of male and female sheep are categorized into three age groups: young, adolescent, and adult. The processes of fuzzification, fuzzy inference, and defuzzification are used to model the data and produce more accurate population predictions. Fuzzy rules are applied to consider the combination of membership levels of each age category and gender. The analysis results indicate that the sheep livestock population in Aceh Tamiang Regency is 5553,69007. The Tsukamoto Fuzzy Logic method has proven effective in handling uncertainty and variability in livestock population data, providing flexibility in complex data-driven decision-making. This study makes a significant contribution to the utilization of fuzzy logic methods for planning and managing livestock populations and can serve as a reference for policymakers in the livestock sector.

Model reference adaptive controller with interpretable fuzzy rules for linear MIMO systems

This paper presents a method for fuzzy model reference adaptive control system design for a partially known multi-input multi-output (MIMO) linear dynamic plant. Differential or convolutional equations can describe the plant to be controlled, and the fuzzy controller is assumed to be from the MIMO sector-bounded nonstationary nonlinearities class. The task is to obtain robust adaptive stabilization. Some absolute stability theorems and integral evaluations of the state and control vectors are applied in the MIMO fuzzy adaptive controller design procedure for the first time. A method for automatically obtaining a near-optimal nonlinear dynamic reference model is described. A practical step-by-step procedure of the MIMO adaptive fuzzy controller design is proposed, resulting in highly interpretable MIMO fuzzy control rules based on triangular fuzzy sets and strong triangular fuzzy partition. An example of the fuzzy controller design according to the proposed procedure is given.