Selection Of Membership Functions Research Articles

Comparative analysis of PI and fuzzy logic controller for grid connected wind turbine under normal and fault conditions

This research is dedicated to improving the control system of wind turbines (WT) to ensure optimal efficiency and rapid responsiveness. To achieve this, the fuzzy logic control (FLC) method is implemented to control the converter in the rotor side (RSC) of a doubly fed induction generator (DFIG) and its performance is compared with an optimized proportional integral (PI) controller. The study demonstrated an enhancement in the performance of the DFIG through the utilization of the proposed FLC, effectively overcoming limitations and deficiencies observed in the conventional controllers, this approach significantly improved the performance of the wind turbine. Additionally, the selected membership functions were found to be highly compatible with the unique characteristics of wind energy. The optimization process is implemented for the controllers of both the grid side converter (GSC) and RSC. Through simulated analyses conducted using MATLAB/Simulink software, comprehensive assessments are carried out. The robustness of the FLC is evaluated compared to the optimized controllers across various wind profiles and challenging fault conditions. The results demonstrate satisfactory performance of the FLC in terms of steady-state time, stability, and precision under diverse wind speed profiles. The FLC achieves a significantly better settling time than the enhanced PI, improving by approximately 14–70% under normal conditions and 40–70% under various fault conditions. Additionally, the FLC outperforms the enhanced PI in fault conditions by reducing peak-to-peak oscillations by about 30–65%. It also delivers a smaller steady-state error, with improvements of around 2–4% under both normal conditions and most fault scenarios.

Comparative Analysis of Fuzzy Logic Models for Depression Prediction: Python and LabVIEW Approaches

Depression is one of the mental disorders with a significant impact on individuals' quality of life and productivity. The diagnostic process for depression, which typically relies on subjective assessment, often encounters challenges of uncertainty and variability in symptoms. This study aims to develop a fuzzy model for predicting depression levels based on five primary symptom variables: worthlessness, concentration, suicidal ideation, sleep disturbance, and hopelessness. The model is implemented on two platforms, Python and LabVIEW, to evaluate the accuracy and consistency of prediction results between these platforms. The analysis process begins with data preprocessing, input variable fuzzification, inference using 243 fuzzy rules, and defuzzification to generate a crisp output value classified into four depression levels: No Depression, Mild, Moderate, and Severe. The study results indicate a very small error margin between the two platforms, with error values below 0.01 in each trial. These findings suggest that both Python and LabVIEW can produce nearly identical and consistent predictions. This conclusion supports the effectiveness of fuzzy logic in addressing uncertainty in clinical data, especially for cases of depression with varying symptoms. Nonetheless, there are limitations related to the subjectivity in selecting membership functions and rules, as well as limitations in the number of variables used. Therefore, this study recommends expanding the developed fuzzy model with additional variables or integrating it with machine learning approaches to improve prediction accuracy. These findings are expected to serve as a foundation for the development of fuzzy-based systems in future mental health diagnostics.

Efficient parameter estimation in biochemical pathways: Overcoming data limitations with constrained regularization and fuzzy inference

In analytical modeling for biochemical pathways, precisely determining unknown parameters is paramount. Traditional methods, reliant on experimental time course data, often encounter roadblocks — limited accessibility and variable quality — that can significantly impact the algorithm’s performance. In this study, we address these hurdles by unveiling a groundbreaking parameter estimation technique, Constrained Regularized Fuzzy Inferred Extended Kalman Filter (CRFIEKF). This innovative approach eliminates the need for experimental time-course measurements and capitalizes on the existing imprecise relationships among the molecules within the network. Our proposed framework integrates a Fuzzy Inference System (FIS) block to encapsulate these approximated relationships. To fine-tune the estimated parameter values, we employ Tikhonov regularization. The selection of Tikhonov regularization and Gaussian membership functions was based on the Mean Squared Error (MSE) values observed during the parameter estimation process, contrasting our results with those of previous studies.We rigorously tested the proposed approach across various pathways, from the glycolytic processes in mammalian erythrocytes and yeast cells to the intricate JAK/STAT and Ras signaling pathways. The results were impressive, showing a significant similarity (p-value < 0.001) to the outcomes of specific prior experiments. The dynamics of the biochemical networks normalized within the [0, 1] range mirrored the transient behavior (MSE < 0.5) of both in vivo and in silico results from previous studies. In conclusion, our findings highlight the effectiveness of CRFIEKF in estimating the kinetic parameter values without prior knowledge of experimental data within a biochemical pathway in the state-space model. The proposed method underscores its potential as a game-changer in biochemical pathway analysis.

RAGOSRA: Rough attributed goal oriented software requirements analysis method

Goal oriented software requirements analysis method is used for the analysis of elicited functional goals (FGs) and non-functional goals (NFGs) of a system in which goals are decomposed and refined into sub-goals until requirements from the sub-goals are identified. Based on the critical analysis, we found that most of the attention of goal-oriented methods is on the crisp and fuzzy logic during the analysis of the software goals or requirements. In these methods’ prior information about the type of membership function is required; and the selection of membership function depends on the subjective justification. As a result, it lacks objectivity and may affect the ranking values of the goals or requirements during the analysis. Therefore, this paper presents a rough attributed goal-oriented software requirements analysis (RAGOSRA) method in which rough preference matrix has been used to capture the opinions of different stakeholders. The results of the RAGOSRA method are compared by considering the following criteria, i.e., goal types, goal links, types of data used in the analysis, stakeholder perceptions and time complexity with some fuzzy based methods. Based on the time complexity analysis, it is found that RAGOSRA method requires only 24 operations for the selection of goals for the dataset having 3 NFGs and 4 FGs of an institute examination system. On the other hand, FAGOSRA method, fuzzy TOPSIS method, and fuzzy AHP method requires 36, 166, and 240 operations respectively.

Нечеткая логика и интеллектуальное управление инженерными системами зданий

Energy consumption factors in the systems of cooling, heating, air conditioning and lighting in a building have a significant impact on the energy costs. Intelligent energy control methods help modernize the engineering systems of buildings, while using artificial neural networks and fuzzy logic for minimizing energy consumption is espe-cially effective in the operation of buildings. To control energy consumption there was proposed the Mamdani fuzzy inference system, selected membership functions of Gaussian, triangular and trapezoidal shapes in the course of the research, implemented the types and functions of inputs and outputs for engineering systems control subsystems in software. According to the input and output parameters, the following systems were designed: lighting, smart window, HVAC; fuzzy inference tables were built, graphical data analysis was performed. The proposed control solutions for the implementation of fuzzy rules based on linguistic variables make it possible to adapt the building management system to environmental conditions and prevent excessive energy consumption. The study substantiates the choice of energy-consuming parts of the building; when forming control actions, fuzzy logic rules are applied in functional ranges. The fuzzy inference system was shown to generate the solutions in accordance with changing input data, integrated control is implemented, the responses of lighting, heating, ventilation and air conditioning systems are analyzed depending on the input membership function. It is proposed to control the intensity of ambient light using motion sensors, including optical ones. It is shown that the results obtained make it possible to achieve a reduction in lighting energy consumption by 15 - 25%, maximum use of external light, ensuring a comfortable temperature regime, and also lead to implementing the coordinated and integrated control functions

Accuracy of the TOPSIS Method with Different Input Data

Using fuzzy logic in multi-criteria problems allows real-world problems to be modeled with greater accuracy. In addition, it is possible to solve problems in which all data are incomplete. The difficulty may be the choice of an appropriate technique leading to a ranking calculation. Methods fully integrated with fuzzy logic can be used. It is also possible to convert fuzzy data to crisp values and then apply traditional MCDA methods. In this paper, the performance of the TOPSIS method in a crisp and fuzzy environment was analyzed. The approaches of operating fully on uncertain data when calculating preference values were compared with converting data to crisp values using selected membership functions. The research has shown that significant differences and discrepancies in how the alternatives are classified are noticeable in the rankings.

Chaotic Krill Herd with Fuzzy Based Routing Protocol for Wireless Networks

Energy is considered a valuable source in wireless sensor networks (WSN) for effectively improving the survivability of the network. The non-uniform dispersion of load in the network causes unbalanced energy dissipation which can result in network interruption. The route selection process can be considered as an optimization problem and is solved by utilize of artificial intelligence (AI) techniques. This study introduces an energy efficient chaotic krill herd algorithm with adaptive neuro fuzzy inference system based routing (EECKHA-ANFIS) protocol for WSN. The goal of the EECKHA-ANFIS method is for deriving a better set of routes to destination in such a way as to improve the survivability in the wireless networks. Primarily, the ANFIS model utilizes the models of fuzzy logic and neural networks (NN) to effectively select the relay nodes for energy efficient communication. Besides, a group of fuzzy rules with membership functions (MF) are designed for selecting the next hop node in wireless networks based on distinct input parameters. Moreover, the optimal selection of MF takes place by the use of chaotic krill herd algorithm (CKHA). In order to showcase the improved performance of the EECKHA-ANFIS approach, a series of simulations are implemented and outcomes are inspected under several aspects. The extensive result analysis demonstrates the betterment of the EECKHA-ANFIS technique over the existing techniques interms of different measures.

Realizing Deep High-Order TSK Fuzzy Classifier by Ensembling Interpretable Zero-Order TSK Fuzzy Subclassifiers

Although high-order Takagi–Sugeno–Kang (TSK) fuzzy systems have demonstrated their computational advantages and simultaneously circumvent the weakness that the number of rules with the number of input variables and membership functions grows exponentially in both zero-order and first-order TSK fuzzy systems for complex modeling tasks, they still face two serious issues: incapability for a changing environment and no interpretability of the coefficients in high-order polynomial used in the consequent part of each fuzzy rule. In order to circumvent these two challenges, a novel stacked architecture of an interpretable deep higher order TSK fuzzy classifier called DHO-TSK and its deep learning method are proposed by proving the equivalence between a high-order TSK fuzzy classifier and a deep ensemble of interpretable zero-order TSK fuzzy classifiers in this article. DHO-TSK can be built by assembling interpretable zero-order TSK fuzzy classifiers in a special stacked way. Each zero-order TSK fuzzy classifier can be learnt by randomly selecting input features, randomly assigning an antecedent fuzzy subset from a fixed fuzzy partition to each of the selected input features, and then multiplying the output of each TSK fuzzy classifier by a randomly selected feature. Except for the abovementioned solid theoretical equivalence, DHO-TSK is featured in the following aspects: first, the consequent part of each fuzzy rule in DHO-TSK becomes interpretable and the output expression of each layer in DHO-TSK becomes comprehensible due to the adopted stacked ensemble; second, its enhanced classification performance can be achieved in a stacked deep learning way; third, DHO-TSK has its adoptability for changing environments owing to random selection of both features and fuzzy membership functions. Our experimental results on the benchmarking UCI and KEEL datasets and a real dataset indicate the effectiveness of DHO-TSK and its learning method in the sense of both classification performance and interpretability.

An improved fatigue life model for mechanical components considering load strengthening characteristics

AbstractThe small load has fuzziness on the strengthening and damage of the component under the action of random fatigue load. This paper introduces the two‐dimensional membership function in fuzzy mathematics to characterize the relationship between stress amplitude, mean value, and damage below the fatigue limit. An exponential function is used to describe the strengthening effect. Under the two‐dimensional load spectrum, the influence of various levels on the component's life is comprehensively considered, and the fatigue life prediction model under random load is proposed. Through the small sample experiment of the car swing support rod, the relevant parameters of the model are obtained. The difference between the modified two‐dimensional and the two‐dimensional load spectrum is compared in fatigue life prediction results. The rules of membership function selection and parameter setting are summarized, and it makes the estimation of component load spectrum life more reasonable.

An improved differential evolution optimization controller for enhancing the performance of PEM fuel cell powered electric vehicle system

At present, the fuel cell is playing major role in the current automotive industry application. Also, the fuel cell have the features of good reliability, high flexibility, reduced demand for foreign oil, and enhanced environment quality. However, for each and every functioning temperature variation, the Fuel Cell (FC) gives different working peak power points on it nonlinear voltage versus current curves. In this article, an Improved Differential Evolutionary Optimization (DEO) method is included in the Fuzzy Logic Controller (FLC) to enhance the maximum power delivered by the fuel cell stack. Here, the main objective of the DEO technique is to optimize the membership functions of the fuel cell stack input and output variables. Due to the proper selection of membership functions, the proposed MPPT controller gives fast tracing speed, good accuracy, and high sustainability. Also, a high voltage gain, nonisolated dc-dc converter is proposed in this work to enhance the fuel cell output voltage and reduce the current flowing through the consumer loads. The properties of converter are less potential pressure across the switches, extensive output voltage functioning, low supply current ripples, and high supply voltage withstand capability.

Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences.

Adaptive Neuro-Fuzzy Inference System (ANFIS) blends advantages of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) in a single framework. It provides accelerated learning capacity and adaptive interpretation capabilities to model complex patterns and apprehends nonlinear relationships. ANFIS has been applied and practiced in various domains and provided solutions to commonly recurring problems with improved time and space complexity. Standard ANFIS has certain limitations such as high computational expense, loss of interpretability in larger inputs, curse of dimensionality, and selection of appropriate membership functions. This paper summarizes that the standard ANFIS is unsuitable for complex human tasks that require precise handling of machines and systems. The state-of-the-art and practice research questions have been discussed, which primarily focus on the applicability of ANFIS in the diversifying field of engineering sciences. We conclude that the standard ANFIS architecture is vastly improved when amalgamated with metaheuristic techniques and further moderated with nature-inspired algorithms through calibration and tuning of parameters. It is significant in adapting and automating complex engineering tasks that currently depend on human discretion, prominent in the mechanical, electrical, and geological fields.

Economic-mathematical model for complex risk assessment of the enterprise investment project using fuzzy logic

The article proposes an economic-mathematical model for determining a comprehensive risk assessment of the investment project of the enterprise which are based on the approaches of A. Nedosekin. The model is built using fuzzy logic and takes into account the probability of occurrence of each of the identified risks and the level of impact of each of them on the project. The probability of risk is set by experts in the form of points and converted into linguistic terms, and the level of influence of each of them on the project – the ratio of benefits and is determined using Fishburne scales. The proposed Project Risk Model consists of the following stages: formation of initial data using expert opinions; construction of a hierarchical project risk tree; determination of weight coefficients (Fishburne weights) of project risks; selection and description of membership function and linguistic variables; conversion of input data provided by experts from a score scale into linguistic terms; recognition of qualitative input data on a linguistic scale; determination of a complex indicator of investment project risks; interpretation of a complex indicator. The developed model allows managing the risks of the project to maximize the probability of its successful implementation, to compare alternative projects and choose less risky, to minimize the level of unforeseen costs of the project.

Fuzzy inference based contextual dissimilarity histogram equalization algorithm for image enhancement

AbstractIn order to overcome the drawback of the existing image enhancement technologies and further consider the pixel intensity expression error caused by imaging, a novel fuzzy inference‐based contextual dissimilarity histogram equalization (FICDHE) algorithm is proposed. The proposed algorithm is composed of three modules. In the first module, according to the calculated probable intensity intervals, the membership functions of intensity are generated. In the second one, fuzzy inference systems are established and the contextual dissimilarity of each pixel is calculated. In the third module, the contextual dissimilarity histograms are clipped and equalized. The fuzzy system established in this paper not only fully considers the uncertainty source of pixel gray level expression, but also has adaptability. The parameter selection of fuzzy inference system membership function in this algorithm does not need human intervention, but is automatically obtained based on the statistical information of image pixel gray. Its adaptability makes the algorithm more widely used and convenient. Experiments are conducted using four typical medical images and 800 images from the KNIX dataset and BrainWeb dataset. The performance of the proposed method was compared to a series of enhancement algorithms based on both subjective judgment and image quality measurement indexes. Experimental results demonstrate that the proposed algorithm has a better contrast enhancement ability and yields better performance.

A survey: fuzzify parameters and membership function in electrical applications

The unique simplicity and the needless for a mathematical model of the fuzzy logic controller along with the variety of abilities such as good respond to complex and high-order systems have made it widely used in electrical engineering. Meanwhile, the influence of fuzzification and defuzzification parameters selection on the quality of the controller performance improvement is obvious. The selection of fuzzy controller parameters is based on the nature of the system and the designer’s knowledge and experience and heretofore no organized method for selecting membership functions has been provided. Given the significance of the issue, this study provides an extensive overview of the fuzzy parameters selection and presents the experiences of dozens of designers in the field of electrical systems. Finally, a summary is presented based on researches for the applicable selection of membership functions in electrical engineering.

FRand: MATLAB Toolbox for Fuzzy Random Number Simulation

Context: This paper presents a MATLAB code implementation and the GUI (General User Interface) for fuzzy random variable generation. Based on previous theoretical results and applications, a MATLAB toolbox has been developed and tested for selected membership functions.&#x0D; Method: A two–step methodology was used: i) a MATLAB toolbox was implemented to be used as interface and ii) all .m functions are available to be used as normal code. The main goal is to provide graphical and code–efficient tools to users.&#x0D; Results: The main obtained results are the MATLAB GUI and code. In addition, some experiments were ran to evaluate its capabilities and some randomness statistical tests were successfully performed.&#x0D; Conclusions: Satisfactory results were obtained from the implementation of the MATLAB code/toolbox. All randomness tests were accepted and all performed experiments shown stability of the toolbox even for large samples (&gt;10.000). Also, the code/toolbox are available online.&#x0D; Acknowledgements: The authors would like to thank to the Prof. M Sc. Miguel Melgarejo and Prof. Jos´e Jairo Soriano–Mendez sincerely for their interest and invaluable support, and a special gratefulness is given to all members of LAMIC.

Fuzzy controller design of micro-unmanned helicopter relying on improved genetic optimization algorithm

In this paper, the dynamic model of unmanned helicopter is modeled by frequency domain identification method. An adaptive fuzzy Proportion Integration Differentiation (PID) controller is established, with its design carried out from the aspects of the input and output fuzzy subset, the membership function selection, the fuzzy rule generation, as well as the fuzzy reasoning. An improved genetic algorithm is employed to optimize the initial expert empirical fuzzy rules, which avoids the traditional method from falling into the local optimal solution in the process of optimization. Specifically, adaptive crossover and mutation probability are adopted to accelerate the convergence speed of the genetic algorithm. We then apply the proposed algorithm to optimize the membership function and fuzzy control rules of the fuzzy controller. Simulation results indicate that our adaptive fuzzy PID has better control effect and anti-interference ability than the traditional PID control law. Additionally, our adaptive fuzzy PID controller is verification by flight test thought AF25B unmanned helicopter platform.

Investigation on adaptive preview distance path tracking control with directional error compensation

Path tracking control cannot effectively satisfy the stability requirements of intelligent vehicles under large curvature conditions. To solve this problem, an adaptive preview distance path tracking controller with a hierarchical structure is proposed in this study. The vehicle centroid lateral acceleration and lateral error of the preview point are taken as the inputs of the upper controller, and the optimal preview distance is obtained based on fuzzy inference. To eliminate the subjective influence of the membership function and fuzzy rule selection in the fuzzy controller design, a genetic algorithm is used for optimization. The lower controller is a sliding mode controller that aims to achieve intelligent vehicle self-tracking. Moreover, a radial basis function neural network is adopted to combine with the sliding mode controller to eliminate output chattering. However, adaptive adjustment of the preview distance deteriorates the vehicle directional tracking error, which makes controlling the vehicle at the road curvature switching point difficult. Thus, a directional error compensation controller is designed based on the iterative learning theory to compensate the front wheel steering angle. Simulations under two standard conditions are carried out to verify the control effect. The results show that, in a double lane change test, the peak centroid acceleration and coaxial load transfer rates decreased by 26.91% and 19.83% at low velocity, respectively, and the improvements at high velocity were 42.71% and 39.22%, respectively. In the pylon course slalom test, all three performance indicators decreased by more than 30%, which indicates the modified adaptive preview distance path tracking controller with a hierarchical structure can effectively improve the vehicle handling performance and roll stability and can ensure the tracking accuracy.

Моделювання інвестиційної політики банку методами нечіткої логіки

Проаналізовано моделі, які використовують в банківській діяльності, починаючи від робіт Еджворта до моделей нечіткої логіки. Зазначено їх переваги та недоліки. Обґрунтовано вибір моделі нечіткої логіки як інструменту моделювання. Розглянуто задачу оцінки інвестиційної політики банку, для якої визначено вид функції приналежності, що є одним із найважливіших питань у теорії нечітких множин. За функцію приналежності обрано несиметричну функцію Гауса, що зумовлено специфікою побудови таких функцій економічних показників, для яких у багатьох випадках відомо тільки три значення: найімовірніше, максимально та мінімально можливі. Запропоновано використати несиметричну функцію приналежності, сформовану на основі функції Гауса з різними значеннями середньоквадратичного відхилення. Проведено чисельні експерименти оцінки інвестиційного проекту на основі трикутних функцій приналежності та запропонованих несиметричних гаусоїд. Для розрахунку параметрів несиметричної функції Гауса спочатку побудовано наближення – трикутна функція приналежності. Виходячи з параметрів трикутної функції, розраховано середньоквадратичні відхилення для лівої та правої частин функції Гауса. Показано, що під час використання несиметричної функції Гауса потрібні більші початкові інвестиції для того, щоб проект був прибутковим. Зроблено висновок щодо необхідності ретельного підбору виду функцій приналежності для зменшення інвестиційного ризику.

Selection of Fuzzy Controller Membership Functions Based on Adaptive Gaussian Cloud Transform

Aiming at the boundedness of existing methods of selecting membership functions, an adaptive Gaussian cloud transform algorithm which is guided by the threshold values of hybridization degree is proposed to construct concept hierarchy from original sample data, and then the number, shape and coverage area of membership functions can be derived from the distribution of Gaussian cloud. To test and verify the effectiveness of membership function that is extracted based on adaptive Gaussian cloud transform algorithm, a six-degree-of freedom model of unmanned aerial vehicles(UAV) is constructed, and a fuzzy controller of pitching angle is established with the platform of Simulink. The simulation results show that the fuzzy controller which includes membership functions derived from the distribution of Gaussian cloud transform can achieve perfect control performance of pitching angle and meanwhile obtain good dynamic response characteristics.

Adaptive neuro-fuzzy approach for modeling equilibrium speed–density relationship

ABSTRACTThis paper endeavors to model the equilibrium speed–density relationship using a new fuzzy logic-based approach. To capture the randomness in traffic flow dynamics, it develops a single-input Adaptive Neuro-Fuzzy Inference System (ANFIS) trained with a hybrid algorithm. Furthermore, it proposes a new Premise-Consequent Conjugate Effect (PCCE) relationship to estimate fundamental diagram (FD) parameters from the ANFIS model. Several options (i.e. optimal split of data into training and testing data, selection of suitable membership function) offered by ANFIS are then illustrated. An experiment is performed to determine the maximum achievable goodness of fit without the occurrence of overfitting phenomenon by changing the number of clusters. The calibrated ANFIS model is compared against traditional and advanced speed–density models for five different freeway locations. Results show that the proposed model outperforms other preceding models by attaining goodness-of-fit values within a range of 0.82–0.92. Finally, the proposed PCCE relationship shows the ANFIS model's robustness in accurate estimation of FD parameters for all freeway locations.