Traditional Fuzzy Logic Research Articles

Improved fuzzy energy management strategy for fuel cell vehicles based on IPIO

In the energy management of fuel cell vehicles (FCEV), the traditional fuzzy logic strategy has the problems of strong subjectivity and average vehicle economy. This paper proposes a composite fuzzy energy management strategy with the goal of improving the life of the auxiliary energy source power battery, using an improved pigeon swarm optimization algorithm (IPIO) to update the fuzzy membership function, while ensuring that the power battery works in a suitable range for a long time and reducing equivalent hydrogen consumption quantity. This paper conducts secondary development on the existing ADVISOR model to establish a simulation model of the FCEV hybrid system, and conducts simulation experiments under NEDC and CLTC-P operating conditions. The results show that IPIO's improved energy management strategy can charge more than twice as fast as the power following strategy when the initial SOC is low, and can reach the appropriate SOC range faster, which can extend the life of the power battery; when the initial SOC is high, the charging speed is more than twice that of the power following strategy. In this case, the equivalent hydrogen consumption of IPIO's improved composite fuzzy energy management strategy was reduced by 9.09% and 9.09% respectively in the two working conditions compared with the before improvement 11.8%, which significantly reduced the hydrogen consumption and improved the economy of hydrogen fuel cell vehicles.

A hybrid-fuzzy-decision-making framework for digital technologies selection

In the era of Industry X.0, manufacturers are increasingly adopting digital technologies such as robotics, big data, mobile communications, and information technology to enhance productivity and profitability. However, navigating this digital revolution presents significant challenges, particularly in selecting the most suitable technologies amidst uncertainties and the lack of precise quantitative data. Fuzzy logic, initially introduced to manage vagueness and imprecision, has proven to be an essential tool in various domains for handling these uncertainties. However, as systems and environments become more complex, traditional fuzzy logic exhibits limitations in effectively representing and managing deeper levels of uncertainty. This realization has driven the development of several extensions to fuzzy logic, designed to address challenges associated with uncertainty representation. Yet, a central question remains: Which fuzzy logic extension is the most relevant for better dealing with uncertainty? This study introduces a hybrid-fuzzy decision-making framework for technology selection, which allows for more than one representation of fuzzy information. Using a multi-expert, multi-criteria decision-making (MCDM) strategy, the study seeks to support organizations in selecting the most suitable digital technology for their operations, particularly when precise quantitative data is unavailable, and decisions must rely on expert opinions. The suggested framework is designed to handle decision-makers' hesitancy, subjectivity, and ambiguity in the decision-making process. To showcase the practical application of the proposed methodology, a case study was conducted to select appropriate technologies in the Moroccan automotive sector. The results demonstrate that, regardless of the fuzzy logic extension used, digital automation and automatic identification consistently rank among the top technologies, underscoring their importance according to most experts. Our proposed approach is particularly effective in resolving conflicts between different fuzzy logic extensions, thereby facilitating the ranking of the remaining technologies. Notably, additive manufacturing ranked third, followed by the other technologies.

Fuzzy logic framework for financial distress prediction: Enhancing corporate decision-making under uncertainty

This research aims to develop an enhanced Fuzzy Logic Framework for Financial Distress Prediction to improve corporate decision-making under uncertainty. The primary objective is to address limitations in traditional fuzzy logic models, such as static rule bases and lack of adaptability to dynamic financial conditions. To achieve this, a time-dependent fuzzy logic system is proposed, incorporating real-time financial data and adaptive learning mechanisms to improve predictive accuracy over time. The research design involves creating a dynamic fuzzy rule base, assigning weights to rules based on predictive performance, and optimizing membership functions and rule weights using real-time data. The methodology applies the proposed framework to financial indicators such as liquidity, profitability, and leverage, with a numerical example demonstrating the system's effectiveness in predicting financial distress. The results show that the model can accurately predict financial distress levels, with a predicted distress value of 0.588 compared to an actual value of 0.6. The model’s ability to update rule weights and optimize predictions over time represents a significant improvement over static fuzzy logic models. This research fills a critical gap in financial distress prediction by introducing a dynamic, adaptive fuzzy logic framework that evolves with real-time data. The model offers significant implications for both academics and industry, providing a tool for more accurate risk assessment in volatile financial environments. However, further research is needed to refine the model’s computational efficiency and test its long-term predictive capabilities across different industries

A Fused Deep Fuzzy Neural Network Controller and Its Application to Pneumatic Flexible Joint

With multilayered deep neural networks (DNNs) distinguished from the traditional single-layer neural networks (SNNs) by the depth, deep learning is a powerful paradigm possessing the capability of feature learning from big data. However, as a completely deterministic model, deep learning does not perform well in dealing with uncertain information and must be pretrained before being applied, which hinders the broad application of deep learning in real-time control. In this article, we introduce the knowledge-based fuzzy logic system (FLS) into deep learning and propose a fused deep fuzzy neural network (FDFNN) controller. The FDFNN controller derives the control signal from both deep fuzzy neural networks and the knowledge-based FLS. At the beginning of control, the FDFNN controller performs like a traditional fuzzy logic controller (FLC), which eliminates the pretraining procedure of deep learning. With another FLS providing supervised signal, the network weights of DFNN's hidden layers can be updated during the control process, which endows the FDFNN controller with online learning capability. A Lyapunov-based convergence analysis is conducted to guarantee the stability of the learning procedure. Both numerical simulations and real-world experiments validate that the proposed method effectively improves the real-time tracking control performance of a pneumatic flexible joint system.

Scaled Conjugate-Artificial Neural Network-Based novel framework for enhancing the power quality of Grid-Tied Microgrid systems

The exponential increase in the dependence on electrical power by rapid industrialization and increasing population has given rise to the need of supplying electric power efficiently and with good power quality (PQ). The evolution of renewable energy sources and their increased penetration into traditional grid systems has increased the complexity of electric power transmission and distribution. The significant increase in the use of complex converters, Flexible AC Transmission System devices and complex nonlinear loads in modern power systems have amplified the issues associated with the production and distribution of electricity having proper PQ. The proper functioning of the power system network requires healthy quality of power, maintained both at the load and source end. The Dynamic Voltage Restorer is a prospective D-FACTS (Distribution Flexible AC Transmission System) device that has been widely incorporated for addressing the PQ issues caused by irregularities in the distribution grid's voltage, current, or frequency. This research article attempts to enhance PQ indices of Photovoltaic, Fuel Cell, DVR and energy storage (battery) based Microgrid systems through a proposed Scaled Conjugate based Artificial Neural Network (SC-ANN) in the Matlab/Simulink architecture. The suggested technique’s effectiveness is verified by introducing severe PQ faults such as sag and swell and numerous power system responses have been comprehensively studied by comparing them with traditional Fuzzy Logic Controller and Proportional Integral controllers. The results obtained validates the efficiency of the proposed controller over FLC and PI methods in maintaining the system power indices constant during the onset of PQ faults by reducing harmonics and oscillations thereby enhancing the overall system reliability, efficiency and stability paving its way for real-time implementation in the areas of MG system.

Research on multilayer fast equalization strategy of Li-ion battery based on adaptive neural fuzzy inference system

In this paper, an adaptive neuro fuzzy inference system (ANFIS) based multilayer fast equalization strategy for Li-ion batteries is developed to solve the problem of wrong fuzzy rule settings affecting the equalization speed in traditional fuzzy logic methods. Firstly, a multilayer equalization circuit is designed to perform both intra-group and inter-group equalization, and a Graph theory analysis shows that this circuit topology can perform fast equalization while ensuring better equalization efficiency. Then an adaptive neuro fuzzy inference system is designed based on the multilayer equalization circuit designed in this paper to achieve more accurate adaptive adjustment of the equalization current and reduce time consumption, and finally a comparative study of the equalization circuit and equalization method under static and charging/discharging conditions is conducted using the Matlab/Similink platform. The experimental study shows that the speed of the multilayer equalization circuit designed in this study is increased by about 62 % and 37.55 % compared with the traditional single-inductor equalization circuit and adjacent-inductor circuit, respectively. Compared with the conventional fuzzy logic control (FLC) equalization strategy, the speed of the proposed equalization strategy is improved by about 10.98 %. It effectively speeds up the equalization process of Li-ion batteries.

Research on the Intelligent Energy Governance of Parallel Hybrid Vehicle Based on Deep Learning

To realize the intelligent energy governance of hybrid vehicles, a Deep-Q-Network energy controller based on the construction of parallel hybrid vehicle model is proposed, which aiming at energy loss problem of parallel hybrid vehicles and combining deep learning with reinforcement learning, and it is simulated through the ADVISOR software platform and compared with the traditional fuzzy logic strategy. The experimental results indicate that the DQN-based control strategy proposed in this paper reduces both the energy consumption and exhaust emissions of parallel hybrid vehicles. Compared with the traditional fuzzy control strategy, fuel consumption is reduced by 0.43L while the fuel economy increases by 10.9%. and exhaust gas such as CO44, CO, NOxx the emission were reduced by 28.9%, 0.2%, and 7.4%, respectively. It shows the feasibility and effectiveness of the proposed methods.

A Self-Organized Method for a Hierarchical Fuzzy Logic System Based on a Fuzzy Autoencoder

In this article, a novel design of a hierarchicalfuzzy system (HFS) based on a self-organized fuzzy partition and fuzzy autoencoder is proposed. The initial rule set of the system is empty, and all the fuzzy sets and fuzzy rules are generated by a self-organized fuzzy partition algorithm. By adopting an improved box plot data standardization method, the processed data can more accurately represent the distribution characteristics of the input data, which improve the accuracy and the rationality. A fuzzy autoencoder is used to train the HFS layer by layer, which can not only ensure the effectiveness of the fuzzy system's hidden layer variables but also provide interpretability. Compared with the traditional fuzzy logic system, the HFS reduces the total number of rules and the complexity. The proposed HFS is tested on three different regression datasets. The experimental results illustrate that the hierarchical self-organized fuzzy system still performs better in terms of regression accuracy indicators than the self-organized fuzzy system.

Analysis of English Cultural Teaching Model Based on Machine Learning

According to the world population, nearly five billion people use mobile phones in their daily lives, and this has increased by 20% in the last twelve months compared to the previous report. An average survey conducted by researchers to find the amount of data consumed in a month by every mobile phone in the world has finally resulted in 45 exabytes of data being collected from a single user within a month. In today's world, data consumption and data analytics are being considered as one of the most important necessities for e-commerce companies. With the help of such collected data from a person, it is possible to predict the future signature or activity of the person. If 45 terabytes of data can be stored for a single user, determining the average calculation and amount of data to be collected for five billion users appears to be much more difficult. More than the human working concept, it looks like it would be difficult for a traditional computer system to handle this amount of data. To study and understand a concept from machine learning and artificial intelligence requires quite a collection of data to predict according to a person's activity. This article explains the roles of faculty and students, as well as the requirements for academic evaluation. Even before the pandemic, most people did not have any idea about the online teaching model. It is only after the disability of conducting direct (offline) classes that people are forced to get into the online world of teaching. Nearly 60% of countries are trying to convert their education systems to such online models, which improve communication between students and teachers and also enable different schemes for students. Big data can be considered as one of the technological revolutions in information technology companies that became popular after the crisis of cloud computing. A support vector machine (SVM) is proposed for analyzing English culture teaching and is compared with the traditional fuzzy logic. The results show the proposed model achieves an accuracy of 98%, which is 5% higher than the existing algorithm.

Fuzzy Logic Controller Optimized by BBO for Decentralized Source Based on a SOFC

This paper will investigate the efficiency of an optimal Fuzzy Logic Controller (FLC) for a decentralized source which is established on the basis of a Solid Oxide Fuel Cell (SOFC) that is linked to the electrical network through a voltage inverter and a boost converter. To serve the purpose of this research, a Biogeography Based Optimization (BBO) is applied in order to adjust the parameters of the membership functions (the centers and the widths of the gaussian membership functions in inputs and output) for the purpose of improving the efficacy of traditional fuzzy logic controller. The given control methods have proved to be effective drawing from simulation results, and show that fuzzy logic controller tuned by biogeography based optimization is better and more robust than the traditional fuzzy logic controller for decentralized source based on a SOFC.

Hyper-Spherical Search Optimized Fuzzy Logic Control Considering Operating Conditions for Hybrid Tram

Rule-based control strategy has significant advantages of strong robustness and high flexibility, and has gradually become a classical implementation method to optimize the energy management performance of modern energy-storage trams. However, it also faces the problems of over-reliance on expert experience and poor adaptability to operating conditions. Therefore, an improved dynamic power distribution method for tram lithium battery/supercapacitor energy storage system is proposed, which introduces road slope and running speed into the decision variable layer of traditional fuzzy logic control input. Firstly, the membership functions and domain are formulated according to operating conditions, the response time of high power density of supercapacitor is adjusted to improve the dynamic performance of the tram. Secondly, the hyper-spherical search algorithm is used to optimize the weight of fuzzy control rules, the peak current of the lithium battery is minimized and the life span of the energy storage system is increased. Finally, the fuzzy control strategies combined with different decision variables are compared and analyzed by using the data of modern tram western suburban line in Beijing. The results show that the proposed strategy achieves multiple superior to traditional fuzzy control strategy in power distribution, charge state offset ranges of lithium battery and supercapacitor, and energy storage system overall efficiency. Compared with the traditional fixed weight scheme, the peak current of the lithium battery is reduced by 31.02% and the continuous tram mileage is increased by 22.45% in the rule weight dynamic optimization scheme.

Wind power smoothing in partial load region with advanced fuzzy-logic based pitch-angle controller

The wind energy system (WES) has an undesirable characteristic in which its power output varies with wind speed, resulting in fluctuations in the grid frequency and voltage. Especially, in partial load region where the wind speed is below rated, there is a concern in regards to WES output power. This part of the work initially employed exponential moving average (EMA) concept to generate reference power. Later on, an interval type-2 fuzzy logic (advanced fuzzy logic) based pitch-angle controller is implemented and designed for good reference tracking and therefore, it can smoothen out the WES output power more effectively. Real time simulations are also developed to show the applicability of the proposed controller using the OPAL-RT digital simulator. Below rated wind speed pattern has considered for real time simulations and results are obtained with different control techniques. The results show that the proposed interval type-2 fuzzy logic (advanced fuzzy logic) based pitch-angle controller offers better performance in tracking reference power and hence, it offers good smoothing of output power fluctuations than conventional proportional-integral (PI) and traditional fuzzy logic (Type-1) controllers. The performance of the proposed controller is also estimated using power smoothing and energy loss functions in terms of performance indices.

Type-2 Fuzzy Logic Systems in Applications: Managing Data in Selective Catalytic Reduction for Air Pollution Prevention

Abstract The article presents our research on applications of fuzzy logic to reduce air pollution by DeNOx filters. The research aim is to manage data on Selective Catalytic Reduction (SCR) process responsible for reducing the emission of nitrogen oxide (NO) and nitrogen dioxide (NO2). Dedicated traditional Fuzzy Logic Systems (FLS) and Type-2 Fuzzy Logic Systems (T2FLS) are proposed with the use of new methods for learning fuzzy rules and with new types of fuzzy implications (the so-called ”engineering implications”). The obtained results are consistent with the results provided by experts. The main advantage of this paper is that type-2 fuzzy logic systems with ”engineering implications” and new methods of learning fuzzy rules give results closer to expert expectations than those based on traditional fuzzy logic systems. According to the literature review, no T2FLS were applied to manage DeNOx filter prior to the research presented here.

Quasi-MV algebras for complex fuzzy logic

<abstract><p>Complex fuzzy logic (CFL) is an emerging topic of research in fuzzy logic. Due to the circle structured codomain of the complex membership function, algebraic structure (including MV-algebra) in traditional fuzzy logic cannot be easily transplanted to CFL. Quasi-MV algebras are almost identical to MV algebras, except $ \alpha\oplus\textbf{0} = \alpha $ does not always hold. In this paper, our goal is to derive some algebraic structures for CFL. We first construct a quasi-MV algebra in complex fuzzy logic by introducing negation and truncated sum in the unit disc of the complex plane $ \textbf{S} $. Next we construct a $ \sqrt{\neg} $ quasi-MV algebra over $ \textbf{S} $ by adding an operation of square root of the negation. Moreover, implication connective and some derived connectives on $ \textbf{S} $ are introduced. Furthermore, we construct a quasi-Wajsberg algebra over $ \textbf{S} $ in which implication is a primitive connective. These algebraic structures are suitable for CFL.</p></abstract>

N-Dimensional (S,N)-implications

The n-dimensional fuzzy logic (n-DFL) has been contributed to overcome the insufficiency of traditional fuzzy logic in modeling imperfect and imprecise information, coming from different opinions of many experts by considering the possibility to model not only ordered but also repeated membership degrees. Thus, n-DFL provides a consolidated logical strategy for applied technologies since the ordered evaluations provided by decision makers impact not only by selecting the best solutions for a decision making problem, but also by enabling their comparisons. In such context, this paper studies the n-dimensional fuzzy implications (n-DI) following distinct approaches: (i) analytical studies, presenting the most desirable properties as neutrality, ordering, (contra-)symmetry, exchange and identity principles, discussing their interrelations and exemplifications; (ii) algebraic aspects mainly related to left- and right-continuity of representable n-dimensional fuzzy t-conorms; and (iii) generating n-DI from existing fuzzy implications. As the most relevant contribution, the prospective studies in the class of n-dimensional interval (S,N)-implications include results obtained from t-representable n-dimensional conorms and involutive n-dimensional fuzzy negations. And, these theoretical results are applied to model approximate reasoning of inference schemes, dealing with based-rule in n-dimensional interval fuzzy systems. A synthetic case-study illustrates the solution for a decision-making problem in medical diagnoses.

Fvsoomm a Fuzzy Vectorial Space Model and Method of Personality, Cognitive Dissonance and Emotion in Decision Making

The purpose of this extension of the ESM’2019 conference paper is to propose some means to implement an artificial thinking model that simulates human psychological behavior. The first necessary model is the time fuzzy vector space model (TFVS). Traditional fuzzy logic uses fuzzification/defuzzification, fuzzy rules and implication to assess and combine several significant attributes to make deductions. The originality of TFVS is not to be another fuzzy logic model but rather a fuzzy object-oriented model which implements a dynamic object structural, behavior analogy and which encapsulates time fuzzy vectors in the object components and their attributes. The second model is a fuzzy vector space object oriented model and method (FVSOOMM) that describes how-to realize step by step the appropriate TFVS from the ontology class diagram designed with the Unified Modeling Language (UML). The third contribution concerns the cognitive model (Emotion, Personality, Interactions, Knowledge (Connaissance) and Experience) EPICE the layers of which are necessary to design the features of the artificial thinking model (ATM). The findings are that the TFVS model provides the appropriate time modelling tools to design and implement the layers of the EPICE model and thus the cognitive pyramids of the ATM. In practice, the emotion of cognitive dissonance during buying decisions is proposed and a game addiction application depicts the gamer decision process implementation with TFVS and finite state automata. Future works propose a platform to automate the implementation of TFVS according to the steps of the FVSOOMM method. An application is a case-based reasoning temporal approach based on TFVS and on dynamic distances computing between time resultant vectors in order to assess and compare similar objects’ evolution. The originality of this work is to provide models, tools and a method to design and implement some features of an artificial thinking model.

A Novel Wind Turbine Fault Detection Method Based on Fuzzy Logic System Using Neural Network Construction Method

Abstract Fuzzy logic system is commonly used in wind turbine fault detection. However, traditional fuzzy logic systems are built through human experience. The fuzzy logic system constructed in this way will have inaccurate problems. In order to solve this problem, this paper proposes a novel fuzzy logic system (FLS) based on neural network construction method to improve accuracy rate of wind turbine fault detection. First, a neural network construction method is proposed. Using this method, membership function can be constructed more accurately. Then, a FLS based on the extended data-driven membership function is proposed. When environment changes, such FLS can improve accuracy rate of wind turbine fault detection with the extended data-driven membership function. Finally, experiments using data from actual wind fields are performed, and the experimental results show that the method proposed in this paper is effective.

An Improved Fuzzy Logic Control Method for Path Tracking of an Autonomous Vehicle

Highlights This study focused on the path tracking accuracy of a two-wheel steering, two-wheel drive autonomous vehicle. An improved fuzzy logic control method (IFM) was designed to control the steering angle of the front wheels. The IFM was better than traditional pure pursuit and fuzzy logic control methods for automatic path tracking. Abstract. This article presents a path-tracking algorithm based on an improved fuzzy logic control method (IFM) that generates optimal steering angles for an autonomous wheeled vehicle to track a sequence of waypoints. The path-tracking algorithm was designed to focus on the vehicle performance, including tracking accuracy, stability, and convergence speed. A prototype vehicle with two-wheel steering and two-wheel drive (2WS-2WD) was developed as a test platform and equipped with a series of sensors for evaluating the IFM. In this study, we considered two variables, i.e., lateral deviation (d) and heading deviation (θ), as inputs for the control system. The output (δ) was the steering angle of the front wheels as controlled by an electric linear actuator. A maximum steering angle strategy was adopted for rapid convergence when the vehicle was far from the desired path or when the heading deviation was large. An update-line distance algorithm was used for finding the best turning time as the vehicle moved closer to the next waypoint. To validate the proposed algorithm, testing was performed to compare traditional pure pursuit (TPP), traditional fuzzy logic control method (TFM), and the IFM in tracking two types of paths (straight and rectangular). Traveling at 0.5 m s-1 and with a signal sampling period of 0.5 s, the prototype converged to the desired straight path from different initial states. When the vehicle reached a stable state, the steady-state error was between 5.0 and 6.5 cm with a mean of ≤6.0 cm. The settling distance in the straight path test was between 125.0 and 422.3 cm, and the convergence time was in the range of 11.5 to 16.0 s; the greater the initial deviation of the vehicle was, the greater the settling distance and settling time were. In the 15 m x 14 m rectangular path test, the overall mean error of the IFM was 14.4 cm when the initial lateral and heading deviations were 0, and the maximum deviation was 67.4 cm, which occurred at the corners. The test results show that the proposed IFM had better accuracy, stability, and convergence speed for path tracking when compared to the TPP and TFM algorithms.

The Optimization of Marine Diesel Engine Rotational Speed Control Process by Fuzzy Logic Control Based on Particle Swarm Optimization Algorithm

The marine main diesel engine rotational speed automatic control plays a significant role in determining the optimal main diesel engine speed under impacting on navigation environment conditions. In this article, the application of fuzzy logic control theory for main diesel engine speed control has been associated with Particle Swarm Optimization (PSO). Firstly, the controller is designed according to fuzzy logic control theory. Secondly, the fuzzy logic controller will be optimized by Particle Swarm Optimization (PSO) in order to obtain the optimal adjustment of the membership functions only. Finally, the fuzzy logic controller has been completely innovated by Particle Swarm Optimization algorithm. The study results will be represented under digital simulation form, as well as comparison between traditional fuzzy logic controller with fuzzy logic control–particle swarm optimization speed controller being obtained.

Design and Implementation of the Permanent- Magnet Synchronous Generator Drive in Wind Generation Systems

The design and implementation of the permanent-magnet synchronous generator drive in wind generation systems is presented in this paper. The permanent-magnet synchronous generator (PMSG) can converse the alternating current (AC) power of the wind turbine to direct current (DC) power. In this paper, the dynamic model of a PMSG is first introduced. The current controller is designed based on T-S fuzzy models of the PMSG. The stability of the proposed PMSG drive system is analyzed and proved. The proposed T-S fuzzy current control possesses a disturbance suppression ability. Compared with the traditional fuzzy logic system, its stability can be proved and verified. Finally, the control performance of the PMSG drive is verified by experimental results.