To see the other types of publications on this topic, follow the link: Fuzzy logic (FL).
Journal articles on the topic 'Fuzzy logic (FL)'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Fuzzy logic (FL).'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Ab Ghani, Hadhrami, and Suraya Syazwani Mohamad Yusof. "FUZZY LOGIC-EMBEDDED MODEL WITH MACHINE LEARNING FOR TRAFFIC CONGESTION PREDICTION." PROCEEDING AL GHAZALI International Conference 2 (January 21, 2025): 484–95. https://doi.org/10.52802/aicp.v1i1.1358.
Full textAbstract:
This study explores the application of fuzzy logic-embedded machine learning models for traffic congestion classification and prediction. The main objective is to compare the performance of a Fuzzy Logic-Embedded Long Short-Term Memory (FL LSTM) model, a Fuzzy Logic-Embedded Random Forest (FL RF), and a Fuzzy Logic-Embedded Support Vector Machine (FL SVM) for predicting traffic congestion levels. A simulated dataset, incorporating features such as traffic volume, vehicle speed, and road occupancy, was used to train and test the models. Results indicated that the FL RF model outperformed both FL LSTM and FL SVM in terms of accuracy, with the highest classification accuracy and lowest misclassification rates observed in the confusion matrix. The FL LSTM model, while effective in capturing temporal dependencies, plateaued in accuracy, while the FL SVM struggled to differentiate between certain congestion levels. The performance of FL RF is attributed to its robustness in handling high-dimensional data and noise, which is crucial for real-world traffic prediction. This study highlights the potential of integrating fuzzy logic with machine learning to handle uncertainty and imprecision in traffic data and suggests that future work could focus on incorporating deep learning techniques for further improvements in accuracy and real-time prediction capabilities.
2
Mohamad, Azizah, Azlan Mohd Zain, and Noordin bin Mohd Yusof. "Overview of Fuzzy Logic Technique for Modeling Machining Process." Applied Mechanics and Materials 815 (November 2015): 264–67. http://dx.doi.org/10.4028/www.scientific.net/amm.815.264.
Full textAbstract:
This paper describes an overview of Fuzzy Logic (FL) application for solving machining problems. The developed fuzzy prediction model is an essential operational guideline for machinist in decision making and adjusting process parameters. This paper also discussed the previous literature that applied the FL in modeling machining process.
3
Rathedi, Maemo, Oduetse Matsebe, and Nonofo M. J. Ditshego. "Performance Evaluation of Hydroponics Control Systems for pH, Temperature, and Water Level Control." International Journal of Engineering Research in Africa 65 (August 8, 2023): 105–16. http://dx.doi.org/10.4028/p-rbt3yu.
Full textAbstract:
This study evaluates different control algorithms used in a hydroponic farming system to improve the quality of farm produce and resource efficiency. It focuses on three key hydroponic control parameters(potential hydrogen (pH), water level, and temperature control). Mathematical models are derived from the literature to represent hydroponic environments. These models are used for simulation purposes in MATLAB software to implement various control algorithms to evaluate their performance against each other and the system requirements utilizing transient performance parameters. Transient performance parameters are overshoot, settling time, rise time ,and steady-state error. The various control algorithms are fuzzy logic (FL), Proportional Integral Derivative (PID), and Proportional Integral Derivative-Fuzzy logic controller (PID-FL). This paper examines the performance of the hybrid PID-FL controllers compared to the most commonly used fuzzy logic and PID controllers. The result of the work shows that PID-FL is generally better for all the system models, making it more applicable.
4
Moreno-Palacio, Diana P., Carlos A. Gonzalez-Calderon, John Jairo Posada-Henao, Hector Lopez-Ospina, and Jhan Kevin Gil-Marin. "Entropy-Based Transit Tour Synthesis Using Fuzzy Logic." Sustainability 14, no. 21 (2022): 14564. http://dx.doi.org/10.3390/su142114564.
Full textAbstract:
This paper presents an entropy-based transit tour synthesis (TTS) using fuzzy logic (FL) based on entropy maximization (EM). The objective is to obtain the most probable transit (bus) tour flow distribution in the network based on traffic counts. These models consider fixed parameters and constraints. The costs, traffic counts, and demand for buses vary depending on different aspects (e.g., congestion), which are not captured in detail in the models. Then, as the FL can be included in modeling that variability, it allows obtaining solutions where some or all the constraints do not entirely satisfy their expected value, but are close to it, due to the flexibility this method provides to the model. This optimization problem was transformed into a bi-objective problem when the optimization variables were the membership and entropy. The performance of the proposed formulation was assessed in the Sioux Falls Network. We created an indicator (Δ) that measures the distance between the model’s obtained solution and the requested value or target value. It was calculated for both production and volume constraints. The indicator allowed us to observe that the flexible problem (FL Mode) had smaller Δ values than the ones obtained in the No FL models. These results prove that the inclusion of the FL and EM approaches to estimate bus tour flow, applying the synthesis method (traffic counts), improves the quality of the tour estimation.
5
Abdalla, M. O., and T. A. Al–Jarrah. "Autogeneration of Fuzzy Logic Rule-Base Controllers." Applied Mechanics and Materials 110-116 (October 2011): 5123–30. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.5123.
Full textAbstract:
A novel Fuzzy Logic controller design methodology is presented. The method utilizes a Particle Swarm Optimization (PSO) binary search algorithm to generate the rules for the Fuzzy Logic controller rule-base stage without human experience intervention. The proposed technique is compared with the well established Lyapunov based Fuzzy Logic controller design in generating the rules. Finally, the controller’s effectiveness and performance are tested, verified and validated using an elevator control application. The novel controller’s results are to be compared with traditional Proportional Integral Derivative (PID) controller and classical Fuzzy Logic (FL) controllers.
6
Tang, Wei-Ling, Jinn-Tsong Tsai, and Yao-Mei Chen. "Fuzzy logic and Gagné learning hierarchy for assessing mathematics skills." Science Progress 104, no. 2 (2021): 003685042110143. http://dx.doi.org/10.1177/00368504211014346.
Full textAbstract:
This study developed a fuzzy logic and Gagné learning hierarchy (FL-GLH) for assessing mathematics skills and identifying learning barrier points. Fuzzy logic was used to model the human reasoning process in linguistic terms. Specifically, fuzzy logic was used to build relationships between skill level concepts as inputs and learning achievement as an output. Gagné learning hierarchy was used to develop a learning hierarchy diagram, which included learning paths and test questions for assessing mathematics skills. First, the Gagné learning hierarchy was used to generate learning path diagrams and test questions. In the second step, skill level concepts were grouped, and their membership functions were established to fuzzify the input parameters and to build membership functions of learning achievement as an output. Third, the inference engine generated fuzzy values by applying fuzzy rules based on fuzzy reasoning. Finally, the defuzzifier converted fuzzy values to crisp output values for learning achievement. Practical applications of the FL-GLH confirmed its effectiveness for evaluating student learning achievement, for finding student learning barrier points, and for providing teachers with guidelines for improving learning efficiency in students.
7
Oladokun, Victor Oluwasina, David G. Proverbs, and Jessica Lamond. "Measuring flood resilience: a fuzzy logic approach." International Journal of Building Pathology and Adaptation 35, no. 5 (2017): 470–87. http://dx.doi.org/10.1108/ijbpa-12-2016-0029.
Full textAbstract:
Purpose Flood resilience is emerging as a major component of an integrated strategic approach to flood risk management. This approach recognizes that some flooding is inevitable and aligns with the concept of “living with water.” Resilience measurement is a key in making business case for investments in resilient retrofits/adaptations, and could potentially be used to inform the design of new developments in flood prone areas. The literature is, however, sparse on frameworks for measuring flood resilience. The purpose of this paper is to describe the development of a fuzzy logic (FL)-based resilience measuring model, drawing on a synthesis of extant flood resilience and FL literature. Design/methodology/approach An abstraction of the flood resilience system followed by identification and characterization of systems’ variables and parameters were carried out. The resulting model was transformed into a fuzzy inference system (FIS) using three input factors: inherent resilience, supportive facilities (SF) and resident capacity. Findings The resulting FIS generates resilience index for households with a wide range of techno-economic and socio-environmental features. Originality/value It is concluded that the FL-based model provides a veritable tool for the measurement of flood resilience at the level of the individual property, and with the potential to be further developed for larger scale applications, i.e. at the community or regional levels.
8
Nithya, S., K. Maithili, T. Sathish Kumar, et al. "A fuzzy logic and cross-layered optimization for effective congestion control in wireless sensor networks to improve efficiency and performance." MATEC Web of Conferences 392 (2024): 01145. http://dx.doi.org/10.1051/matecconf/202439201145.
Full textAbstract:
Wireless Sensor Networks (WSNs) are a fundamental component of the Internet of Things (IoT), used in diverse applications to detect environmental conditions and send information to the Internet. WSNs are susceptible to congestion issues, leading to increased packet loss, extended delays, and reduced throughput. This research introduces a Fuzzy Logic-based Cross-Layered Optimization Model (FL-CLOM) for WSNs to tackle the problem. FL-CLOM is developed by including the signal-to-noise ratio of the wireless channels in the Transmission Control Protocol (TCP) approach, bridging the transmission layer and Media Access Control (MAC) layer. A fuzzy logic system is created by integrating fuzzy control with congestion control to dynamically manage the queue size in crowded nodes and minimize the effects of external uncertainties. Various simulations were conducted using MATLAB and NS-2.34 to compare the suggested FL-CLOM to conventional methods. The results indicate that FL-CLOM efficiently adjusts to queue size changes and demonstrates rapid convergence, reduced average delay, reduced packet loss, and increased throughput.
9
Chen, J. C., and R. Krisshnasamy. "Development of eDART-based weight prediction system in injection molding via Taguchi design and fuzzy logic." Journal of Physics: Conference Series 2631, no. 1 (2023): 012013. http://dx.doi.org/10.1088/1742-6596/2631/1/012013.
Full textAbstract:
Abstract This research describes developing a Fuzzy Logic based weight prediction system (FL-eWPS) during the process of injection molding. The main purpose is to apply Fuzzy Logic to predict defects during injection molding operations while processing parameters, such as shot size, barrel temperature, cooling time, and holding pressure. The parameters are varied within a shorter range when using Delrin 511 DP plastic from DuPont Engineering Polymers. eDART data logging system was used for real-time data collection for the different parameters by using the sensors during the injection filling stages. A Fuzzy Logic reasoning algorithm was applied to gain the threshold values of weight prediction with various processing parameter settings. During the injection molding process, the FL-eWPS system was shown to predict weight with 99% accuracy.
10
Kasmi, B., and A. Hassam. "Comparative Study between Fuzzy Logic and Interval Type-2 Fuzzy Logic Controllers for the Trajectory Planning of a Mobile Robot." Engineering, Technology & Applied Science Research 11, no. 2 (2021): 7011–17. http://dx.doi.org/10.48084/etasr.4031.
Full textAbstract:
In this study, Fuzzy Logic (FL) and Interval Type-2 FL (IT-2FL) controllers were applied to a mobile robot in order to determine which method facilitates navigation and enables the robot to overcome real-world uncertainties and track an optimal trajectory in a very short time. The robot under consideration is a non-holonomic unicycle mobile robot, represented by a kinematic model, evolving in two different environments. The first environment is barrier-free, and moving the robot from an initial to a target position requires the introduction of a single action module. Subsequently, the same problem was approached in an environment closer to reality, with objects hindering the robot's movement. This case requires another controller, called obstacle avoidance. This system allows the robot to reach autonomously a well-defined target by avoiding collision with obstacles. The robustness of the structures of the defined controllers is tested in Matlab simulations of the studied controllers. The results show that the IT-2FL controller performs better than the FL controller.
11
Salami, Ameen M., Dhaideen M. Salih, and Omar N. Jasim. "Fuzzy Logic For Estimating Surgery Risks Based on ASA Classification." Journal of Research in Engineering and Computer Sciences 3, no. 1 (2025): 32–40. https://doi.org/10.63002/jrecs.31.799.
Full textAbstract:
The huge development of artificial intelligence in health care system dealing with uncertainties in the health decision‐making process. The fuzzy logic provides an effective income for dealing with uncertainties. This research practices fuzzy logic (FL) system to think like an expert clinician and exam the possibility of diagnosing the risk of performing surgical operations requiring general anesthesia. This done through the patient's main tests and analyses the levels of test results according to the American Society of General Anesthesia (ASA). Relying on fuzzy logic rules, the possibility of reducing huge input probabilities to four cases of output (no risk and three levels of risk). this were achieved according to the FL most successful Mamdani technique and using the Gaussian function as membership functions. The results showed an efficient result that aid physician committee in making important decisions related to the patient’s life.
12
Nasir, Mohammad, Ali Sadollah, Przemyslaw Grzegorzewski, Jin Hee Yoon, and Zong Woo Geem. "Harmony Search Algorithm and Fuzzy Logic Theory: An Extensive Review from Theory to Applications." Mathematics 9, no. 21 (2021): 2665. http://dx.doi.org/10.3390/math9212665.
Full textAbstract:
In recent years, many researchers have utilized metaheuristic optimization algorithms along with fuzzy logic theory in their studies for various purposes. The harmony search (HS) algorithm is one of the metaheuristic optimization algorithms that is widely employed in different studies along with fuzzy logic (FL) theory. FL theory is a mathematical approach to expressing uncertainty by applying the conceptualization of fuzziness in a system. This review paper presents an extensive review of published papers based on the combination of HS and FL systems. In this regard, the functional characteristics of models obtained from integration of FL and HS have been reported in various articles, and the performance of each study is investigated. The basic concept of the FL approach and its derived models are introduced to familiarize readers with the principal mechanisms of FL models. Moreover, appropriate descriptions of the primary classifications acquired from the coexistence of FL and HS methods for specific purposes are reviewed. The results show that the high efficiency of HS to improve the exploration of FL in achieving the optimal solution on the one hand, and the capability of fuzzy inference systems to provide more flexible and dynamic adaptation of the HS parameters based on human perception on the other hand, can be a powerful combination for solving optimization problems. This review paper is believed to be a useful resource for students, engineers, and professionals.
13
Ahmed, Omer K., Raid W. Daoud, Shaimaa M. Bawa, and Ahmed H. Ahmed. "Optimization of PV/T Solar Water Collector based on Fuzzy Logic Control." International Journal of Renewable Energy Development 9, no. 2 (2020): 303–10. http://dx.doi.org/10.14710/ijred.9.2.303-310.
Full textAbstract:
Hybrid solar collector (PV/T) is designed to produce electricity, hot water, or hot air at the same time as they operate solar cells and solar heaters in one system. This system is designed to increase the electrical efficiency of solar cells by absorbing heat from these cells. The fuzzy logic (FL) is a tool usually used to optimize the operation of the systems. In this paper, the FL is to monitor and correct the mainsystem parameters to remain optimization efficiency at a better level. Three affected variables were studied: Effect of reflective mirrors, the effect of the glass cover, and the effect of the lower reflector angle on the performance of the PV / T hybrid solar system. These three parameters are traveled to be inputs for the FL, and the PV temperature in addition to system efficiency is the output for it. The effect of solar radiation was found to have a great effect on the efficiency of the hybrid solar collector. The thermal efficiency was 82% for the given value of the PV and mirrors, while the efficiency down to 50 for another angle. By using the artificial intelligent the system behavior depends on its output, which called feedback close loop control, at a real-time process that optimizes the system efficiency and its output. ©2020. CBIORE-IJRED. All rights reserved
14
Antigha Ita, Maurice. "Short-Term Electrical Energy Demand Forecasting Using Fuzzy Logic Technique." Arid-zone Journal of Basic and Applied Research 4, no. 1 (2024): 88–102. http://dx.doi.org/10.55639/607.444342.
Full textAbstract:
Minimization of electrical energy wastage and provision of its regular supply make electrical load forecasting an important aspect of the power infrastructure. An accurate load forecasting is crucial for the reduction of the cost of electrical energy generation and spinning reserve capacity. Therefore, in this study, fuzzy logic (FL) technique was employed for the projection of electrical energy demand on short-term basis. The FL model was trained using the six months hourly load and temperature data respectively obtained from 132/33 kV Ikeja West Transmission Station, Ayobo and Nigerian Meteorological Agency, Oshodi, Lagos State, Nigeria. Triangular and trapezoidal membership functions (MFs) were used in the training of the model with Mamdani fuzzy inference system. The time, load and temperature inputs were fuzzified into six, four and three MFs respectively while the fuzzification process used 25 fuzzy rule bases. The centroid method of defuzzification was used to change the results into readable values. The adequacy of the FL model was determined using five metrics including mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), chi square (χ2) and F- test. The obtained results revealed that the FL model developed excelled in all the five tests of adequacy considered with MSE, MAE, MAPE, χ2 and F-test values of 4.17, 6.74, 11.51%, 7.93 and 1.27 respectively and hence, performed satisfactorily. This work established that the fuzzy logic approach is appropriate for electrical load projection on short-term basis.
15
YALÇINKAYA, Fikret, Ata SEVİNC, Hüseyin AYDİLEK, and Ali ERBAŞ. "Ultrasonic Therapy Device Using Fuzzy-Logic for Clinical Use." Uluslararası Muhendislik Arastirma ve Gelistirme Dergisi 15, no. 2 (2023): 776–85. http://dx.doi.org/10.29137/umagd.1310831.
Full textAbstract:
In this paper, an ultrasonic therapy device using fuzzy-logic(UTD-FL) has been designed, constructed and tested with phantom materials. Fuzzy-logic rules have been determined using four parameters. In clinical practice, ultrasonic therapy is conducted solely based on subjective evaluation of medical experts, but in UTD-FL, fuzzy-logic rules decide automatically the three critically important characteristic parameters of applicable output power to the patient, namely the power of the signal, the percent of the duty-cycle and the signal frequency. The signal frequency and specifically its amplitude have critical effect on the temperature rise of the tissue test-point or surface. Therefore the intensity of the ultrasonic frequency and the duration of treatment-time are absolutely vital, that is why this instrument has been developed. This instrument is expected to prevent possible side effects, injuries, and potential damages on real tissues due to experts’ uneasiness. The test results of this newly developed medical device have been compared with clinical-practice. The instrument produces optimum output power due to its fuzzy-logic rules based mode design, and IR-temperature sensor based feedback effect; whereas the clinical mode inputs only clinical experience base gained medical data.
16
Hayder, Yousif Abed, Thiab Humod Abdulrahim, and J. Humaidi Amjad. "Type 1 versus type 2 fuzzy logic speed controllers for brushless DC motors." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 1 (2020): 265–74. https://doi.org/10.11591/ijece.v10i1.pp265-274.
Full textAbstract:
This work presented two fuzzy logic (FL) schemes for speed-controlled brushless DC motors. The first controller is a Type 1 FL controller (T1FLC), whereas the second controller is an interval Type 2 FL controller (IT2FLC). The two proposed controllers were compared in terms of system dynamics and performance. For a fair comparison, the same type and number of membership functions were used for both controllers. The effectiveness of the structures of the two FL controllers was verified through simulation in MATLAB/SIMULINK environment. Simulation result showed that IT2FLC exhibited better performance than T1FLC.
17
Lin, Y. J., Y. Lu, T. Lee, and B. Choi. "Modeling and Fuzzy Logic Control of an Active Reaction Compensating Platform System." Shock and Vibration 2, no. 6 (1995): 493–506. http://dx.doi.org/10.1155/1995/829607.
Full textAbstract:
This article presents the application of the fuzzy logic (FL) concept to the active control of a multiple degree of freedom reaction compensating platform system that is designed and used for isolating vibratory disturbances of space-based devices. The physical model used is a scaled down two-plate platform system. In this work, simulation is performed and presented. According to the desired performance specifications, a full range of investigation regarding the development of an FL stabilization controller for the system is conducted. Specifically, the study includes four stages: comprehensive dynamic modeling of the reaction compensating system; analysis of the dynamic responses of the platform system when it is subjected to various disturbances; design of an FL controller capable of filtering the vibratory disturbances transmitted to the bottom plate of the platform system; performance evaluation of the developed FL controller through computer simulations. To simplify the simulation work, the system model is linearized and the system component parameter variations are not considered. The performance of the FL controller is tested by exciting the system with an impulsive force applied at an arbitrarily chosen point on the top plate. It is shown that the proposed FL controller is robust in that the resultant active system is well stabilized when subjected to a random external disturbance. The comparative study of the performances of the FL controlled active reaction and passive reaction compensating systems also reveals that the FL controlled system achieves significant improvements in reducing vibratory accelerations over passive systems.
18
Tabares, Héctor, and Jesús Hernández. "Forecasting time series. Short-term electrical power using fuzzy logic." Revista Facultad de Ingeniería Universidad de Antioquia, no. 47 (December 4, 2013): 209–17. http://dx.doi.org/10.17533/udea.redin.17784.
Full textAbstract:
In recent developments of fuzzy logic, the paradigm of universal proximity is very common. The fuzzy systems are estimates of a continuous function in a X group. The objective of this work is to find out a F(x) function with Fuzzy Logic (FL) that will allow to map out daily electrical power curves in a residential sector, in the city of Medellín. The variable input vector is the total electricity required for a 24 hour period (t).
19
Aoukach, Basma, and Benyounes Oukarfi. "An improved energy management method based on combination of multi agent system and fuzzy logic." European Physical Journal Applied Physics 92, no. 3 (2020): 30902. http://dx.doi.org/10.1051/epjap/2020200189.
Full textAbstract:
This paper presents a distributed energy management strategy based on a combination of multi agent system (MAS) and fuzzy logic (FL). Our system is composed of two renewable energy sources which are a photovoltaic panel, wind turbine, storage system and a variable loads. These elements are linked between them through switches. This improved technique allows the control of switches to manage the energy flow between sources and established load priority. MAS consider each element of the system as an agent, one of them is called execution agent that controls switches based on the paradigm of fuzzy logic (FL). The results of this approach are presented, discussed then followed by a simulation.
20
Pan, Bin, Hao Wu, and Jin Wang. "FL-ASB: A Fuzzy Logic Based Adaptive-period Single-hop Broadcast Protocol." International Journal of Distributed Sensor Networks 14, no. 5 (2018): 155014771877848. http://dx.doi.org/10.1177/1550147718778482.
Full textAbstract:
In vehicular ad hoc networks, vehicle-to-vehicle–based broadcast can fast disseminate safety messages between vehicles within the whole network and hence expand drivers perception vision, which will reduce the accident probability and ensure the transportation reliability. As for fixed-period single-hop broadcast protocol, disseminating safety messages frequently can cause excessive network load. However, increasing period purely does not guarantee the real-time performance. In addition, exiting adaptive-period single-hop broadcast protocols also have limitations without considering synthetically various impact factors. Thus, how to design a single-hop broadcast protocol that can dynamically adjust the broadcast period according to the actual road condition is a pressing issue. A Fuzzy Logic Based Adaptive-period Single-hop Broadcast Protocol in vehicular ad hoc networks is designed in this article, which provides a new solution for the dissemination of period safety messages. In this article, the impact of various factors (such as the number of one-hop neighbor nodes, vehicle speed, received signal strength index, and visibility) on the single-hop broadcast period has been analyzed. In view of each impact factor, we design corresponding membership function and fuzzy rules according to the specific scenarios and parameters. It realizes the adaptive changes of period safety messages broadcast period through the simulation of the proposed fuzzy logic inference system. Finally, we verify the performance of the Fuzzy Logic Based Adaptive-period Single-hop Broadcast Protocol in a bidirectional four-lane highway scenario. Simulation results show that the proposed Fuzzy Logic Based Adaptive-period Single-hop Broadcast Protocol has obvious advantages in terms of network load ratio, average one-hop delay, and delivery ratio.
21
AsadUllah, Muhammad, Muhammad Adnan Khan, Sagheer Abbas, Atifa Athar, Syed Saqib Raza, and Gulzar Ahmad. "Blind Channel and Data Estimation Using Fuzzy Logic-Empowered Opposite Learning-Based Mutant Particle Swarm Optimization." Computational Intelligence and Neuroscience 2018 (December 6, 2018): 1–12. http://dx.doi.org/10.1155/2018/6759526.
Full textAbstract:
Multiple-input and multiple-output (MIMO) technology is one of the latest technologies to enhance the capacity of the channel as well as the service quality of the communication system. By using the MIMO technology at the physical layer, the estimation of the data and the channel is performed based on the principle of maximum likelihood. For this purpose, the continuous and discrete fuzzy logic-empowered opposite learning-based mutant particle swarm optimization (FL-OLMPSO) algorithm is used over the Rayleigh fading channel in three levels. The data and the channel populations are prepared during the first level of the algorithm, while the channel parameters are estimated in the second level of the algorithm by using the continuous FL-OLMPSO. After determining the channel parameters, the transmitted symbols are evaluated in the 3rd level of the algorithm by using the channel parameters along with the discrete FL-OLMPSO. To enhance the convergence rate of the FL-OLMPSO algorithm, the velocity factor is updated using fuzzy logic. In this article, two variants, FL-total OLMPSO (FL-TOLMPSO) and FL-partial OLMPSO (FL-POLMPSO) of FL-OLMPSO, are proposed. The simulation results of proposed techniques show desirable results regarding MMCE, MMSE, and BER as compared to conventional opposite learning mutant PSO (TOLMPSO and POLMPSO) techniques.
22
Wan, Jian, Peiwen Ren, and Qiang Guo. "Application of Interactive Multiple Model Adaptive Five-Degree Cubature Kalman Algorithm Based on Fuzzy Logic in Target Tracking." Symmetry 11, no. 6 (2019): 767. http://dx.doi.org/10.3390/sym11060767.
Full textAbstract:
Aiming at the shortcomings of low precision, hysteresis, and poor robustness of the general interactive multimodel algorithm in the “snake-like” maneuver tracking of anti-ship missiles, an interactive multimodel adaptive five-degree cubature Kalman algorithm based on fuzzy logic (FLIMM5ACKF) is proposed. The algorithm mainly includes adaptive five-degree cubature Kalman algorithm (A5CKF) and fuzzy logic algorithm (FL). A5CKF uses the Sage–Husa noise estimation principle to propose a state error covariance adaptive five-degree cubature Kalman algorithm to improve the performance of state estimation. Then, the fuzzy logic algorithm (FL) is added to the model probability update module to control the model probability update module. Finally, by setting the same tracking model simulation analysis, the algorithm has better convergence speed, tracking effect and robustness than the interactive multimodel cubature Kalman algorithm (IMMCKF), the interactive multimodel five-degree cubature Kalman algorithm (IMM5CKF) and the interactive multimodel adaptive five-degree cubature Kalman (IMMA5CKF).
23
S.M.A.N.M, Subasinghe. "Leveraging Business-Inspired Computational Intelligence Techniques for Enhanced Data Analytics: Applications of Genetic Algorithms, Fuzzy Logic, and Swarm Intelligence." Leveraging Business-Inspired Computational Intelligence Techniques for Enhanced Data Analytics: Applications of Genetic Algorithms, Fuzzy Logic, and Swarm Intelligence 9, no. 1 (2024): 6. https://doi.org/10.5281/zenodo.10638915.
Full textAbstract:
Data has become a crucial element for contemporary enterprises; however, deriving practical insights from its immense volume remains an intricate obstacle. This paper examines the capabilities of three bio- inspired computational intelligence (CI) methods - Genetic Algorithms (GAs), Fuzzy Logic (FL), and Swarm Intelligence (SI) - in improving data analytics for business optimization and decision-making. The researcher thoroughly examines the fundamental principles of each technique, emphasizing their inherent advantages and appropriateness for addressing practical business challenges. By reviewing recent research and real-world examples, the researcher illustrates how Genetic Algorithms (GAs) can enhance the efficiency of resource allocation, Fuzzy Logic (FL) can effectively handle uncertainty in risk assessment, and Swarm Intelligence (SI) can streamline logistics and scheduling processes. In conclusion, highlight the synergistic and hybrid methods emerging in this field. These approaches are leading to enhanced value extraction from data and pushing the limits of business intelligence. Keywords:- Data Analytics, Business Intelligence, Genetic Algorithms, Fuzzy Logic, Swarm Intelligence, Optimization, Enterprise Decision-Making, Case Studies.
24
Ismaeel, Salam, Ayman Al-Khazraji, and Karama Al-delimi. "Fuzzy Information Modeling in a Database System." IAES International Journal of Artificial Intelligence (IJ-AI) 6, no. 1 (2017): 1. http://dx.doi.org/10.11591/ijai.v6.i1.pp1-7.
Full textAbstract:
A Fuzzy logic (FL) provides a remarkably simple way to draw definite conclusions from vague, ambiguous or imprecise information. In a sense, fuzzy logic resembles human decision making with its ability to work from approximate data and find precise solutions. In this paper a fuzzy information modeling system was developed then used in a database, which contains fuzzy data and real data, to create new information assistance capable of making any decision about this data. The proposed system is implemented on a special database used to evaluation workers or users in any formal organizations.
25
Salam, Ismaeel, Al-Khazraji Ayman, and Al-delim Karama. "Fuzzy Information Modeling in a Database System." IAES International Journal of Artificial Intelligence (IJ-AI) 6, no. 1 (2017): 1–7. https://doi.org/10.5281/zenodo.4107767.
Full textAbstract:
A Fuzzy logic (FL) provides a remarkably simple way to draw definite conclusions from vague, ambiguous or imprecise information. In a sense, fuzzy logic resembles human decision making with its ability to work from approximate data and find precise solutions. In this paper a fuzzy information modeling system was developed then used in a database, which contains fuzzy data and real data, to create new information assistance capable of making any decision about this data. The proposed system is implemented on a special database used to evaluation workers or users in any formal organizations.
26
Thakur, Amey. "Neuro-Fuzzy: Artificial Neural Networks & Fuzzy Logic." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 128–35. http://dx.doi.org/10.22214/ijraset.2021.37930.
Full textAbstract:
Abstract: Neuro Fuzzy is a hybrid system that combines Artificial Neural Networks with Fuzzy Logic. Provides a great deal of freedom when it comes to thinking. This phrase, on the other hand, is frequently used to describe a system that combines both approaches. There are two basic streams of neural network and fuzzy system study. Modelling several elements of the human brain (structure, reasoning, learning, perception, and so on) as well as artificial systems and data: pattern clustering and recognition, function approximation, system parameter estimate, and so on. In general, neural networks and fuzzy logic systems are parameterized nonlinear computing methods for numerical data processing (signals, images, stimuli). These algorithms can be integrated into dedicated hardware or implemented on a general-purpose computer. The network system acquires knowledge through a learning process. Internal parameters are used to store the learned information (weights). Keywords: Artificial Neural Networks (ANNs), Neural Networks (NNs), Fuzzy Logic (FL), Neuro-Fuzzy, Probability Reasoning, Soft Computing, Fuzzification, Defuzzification, Fuzzy Inference Systems, Membership Function.
27
Alashan, Sadık, Sedat Golgiyaz, Erdinç İkincioğulları, and Eyyüp Ensar Yalçın. "Practical Design of Stepped Spillways Using Machine Learning Methods and Fuzzy Inference System." Harran Üniversitesi Mühendislik Dergisi 10, no. 1 (2025): 36–50. https://doi.org/10.46578/humder.1638527.
Full textAbstract:
Energy-dissipating pools or flip bucket structures reduce the energy of downstream flow in conventional spillways. Recently, stepped spillways have been widely used to dissipate the flow of energy downstream. Flows on the stepped spillways are complex and advanced techniques such as Fuzzy Logic (FL), Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), Genetic Programming (GP), Deep Learning, and Tree-Based models are required to calculate energy dissipation ratios. Fuzzy Logic has the advantage of considering physical processes when examining problems using rule bases. In this study, energy dissipation over stepped spillways is calculated using machine learning methods and the Fuzzy Inference System in Python programming language. Experimental data by different researchers are used to model stepped spillways. Two new parameters, such as an approach channel and step-top geometric ratios, are used in addition to the literature to obtain energy dissipation ratios on stepped spillways. Artificial Neural Network Regressor (ANN) from machine learning methods gives minimum percentages and absolute errors (-0.117% and 1.398) and maximum R^2 values (0.976) for the testing dataset. Although the accuracy of the ANN method changes with hidden layer sizes and ratios between training and testing data, the Fuzzy Logic (FL) is independent to training data. The FL method represents good results with low Mean Percentages Error (MPE) and Mean Absolute Errors (MAE) (-1.688% and 2.000) and an R^2 value (0.951), and the produced Python function using the fuzzy inference system can be applied easily to different flow conditions and stepped spillways.
28
Hamzah, Abdulmughni, Mohammad Shurman, Omar Al-Jarrah, and Eyad Taqieddin. "Energy-Efficient Fuzzy-Logic-Based Clustering Technique for Hierarchical Routing Protocols in Wireless Sensor Networks." Sensors 19, no. 3 (2019): 561. http://dx.doi.org/10.3390/s19030561.
Full textAbstract:
In wireless sensor networks, the energy source is limited to the capacity of the sensor node’s battery. Clustering in WSN can help with reducing energy consumption because transmission energy is related to the distance between sender and receiver. In this paper, we propose a fuzzy logic model for cluster head election. The proposed model uses five descriptors to determine the opportunity for each node to become a CH. These descriptors are: residual energy, location suitability, density, compacting, and distance from the base station. We use this fuzzy logic model in proposing the Fuzzy Logic-based Energy-Efficient Clustering for WSN based on minimum separation Distance enforcement between CHs (FL-EEC/D). Furthermore, we adopt the Gini index to measure the clustering algorithms’ energy efficiency in terms of their ability to balance the distribution of energy through WSN sensor nodes. We compare the proposed technique FL-EEC/D with a fuzzy logic-based CH election approach, a k-means based clustering technique, and LEACH. Simulation results show enhancements in energy efficiency in terms of network lifetime and energy consumption balancing between sensor nodes for different network sizes and topologies. Results show an average improvement in terms of first node dead and half nodes dead.
29
Abed, Hayder Yousif, Abdulrahim Thiab Humod, and Amjad J. Humaidi. "Type 1 versus type 2 fuzzy logic speed controllers for brushless dc motors." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 1 (2020): 265. http://dx.doi.org/10.11591/ijece.v10i1.pp265-274.
Full textAbstract:
<span lang="EN-US">This work presented two fuzzy logic (FL) schemes for speed-controlled brushless DC motors. The first controller is a Type 1 FL controller (T1FLC), whereas the second controller is an interval Type 2 FL controller (IT2FLC). The two proposed controllers were compared in terms of system dynamics and performance. For a fair comparison, the same type and number of membership functions were used for both controllers. The effectiveness of the structures of the two FL controllers was verified through simulation in MATLAB/SIMULINK environment. Simulation result showed that IT2FLC exhibited better performance than T1FLC.</span>
30
Wang, Jiyuan, Kaiyue Wang, Xiangfang Yan, and Chanjuan Wang. "A Hybrid Learning Particle Swarm Optimization With Fuzzy Logic for Sentiment Classification Problems." International Journal of Cognitive Informatics and Natural Intelligence 16, no. 1 (2022): 1–23. http://dx.doi.org/10.4018/ijcini.314782.
Full textAbstract:
Methods based on deep learning have great utility in the current field of sentiment classification. To better optimize the setting of hyper-parameters in deep learning, a hybrid learning particle swarm optimization with fuzzy logic (HLPSO-FL) is proposed in this paper. Hybrid learning strategies are divided into mainstream learning strategies and random learning strategies. The mainstream learning strategy is to define the mainstream particles in the cluster and build a scale-free network through the mainstream particles. The random learning strategy makes full use of historical information and speeds up the convergence of the algorithm. Furthermore, fuzzy logic is used to control algorithm parameters to balance algorithm exploration and exploration performance. HLPSO-FL has completed comparison experiments on benchmark functions and real sentiment classification problems respectively. The experimental results show that HLPSO-FL can effectively complete the hyperparameter optimization of sentiment classification problem in deep learning and has strong convergence.
31
Sarojini, Ratnam Kamala, Kaliannan Palanisamy, and Enrico De Tuglie. "A Fuzzy Logic-Based Emulated Inertia Control to a Supercapacitor System to Improve Inertia in a Low Inertia Grid with Renewables." Energies 15, no. 4 (2022): 1333. http://dx.doi.org/10.3390/en15041333.
Full textAbstract:
The contribution of power generation from converter-dominated renewable energy sources (RES) has increased enormously. Consequently, the system inertia is decreasing, and it impacts the frequency of the system. With large-scale integration of power electronic inverter-based power generation from RES, inertia from energy storage devices would be unavoidable in future power grids. In this paper, the inertia emulator is formed with a supercapacitor (IE–SC) to improve inertia in a low inertia grid. To emulate the inertia in a low inertia grid, this paper proposes a fuzzy logic controller-based emulated inertia controller (FL-EIC) for an inverter attached to IE–SC. The proposed fuzzy logic controller estimates the inertial power required based on the frequency deviation and rate of change of frequency (ROCOF). The output of the fuzzy controller adds to the conventional emulated inertia control (EIC) technique to alter the load angle for the power electronic inverter of IE–SC. Specifically, the proposed FL-EIC achieves inertia emulation by proportionally linking the time derivative of the grid frequency and frequency deviation to active power references of IE–SC. A comparison of the conventional EIC and FL-EIC is carried out to prove the effectiveness of the proposed FL-EIC. Furthermore, real-time simulations with the help of the OPAL-RT real-time simulator (OP 5700) are presented to validate the advantage of the FL-EIC.
32
Zhang, Gui Chen. "Design and Simulation of the Self-Tune Fuzzy Logic PID Controller for VAV Air Condition System." Applied Mechanics and Materials 397-400 (September 2013): 1286–90. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.1286.
Full textAbstract:
Base on the Fuzzy Logic(FL) control theorem, the Self-tune FL PID controller is designed in which the base rules of operator experience were combined. In MATLAB simulation, when system uses the same parameters, the Self-tune FL PID controller’s over-adjust decreases by 17% compared to the conventional PID controller. That applying Self-tune FL PID controller to control the damper’s position in VAV system is able to achieve the steady and less energy consumption system.
33
John Rose, A., G. Anandha Kumar, A. Ragavendiran, and A. Kalaimurugan. "Model Predictive Controlled Four-Bus System Employing Hybrid Power Flow Controller." Journal of Sensors 2023 (March 3, 2023): 1–10. http://dx.doi.org/10.1155/2023/4621537.
Full textAbstract:
A fuzzy logic- (FL-) based control technique for unified power flow controller (UPFC) to resolve the power quality issues in transmission network is presented in this paper. MATLAB/Simulink is used to design the FL-based controllers for shunt and series converters of UPFC, which is validated on 4-bus system. In addition, the performance of the suggested FL-based UPFC is compared with PI (proportional integral), HC (hysteresis controller), MPC (model predictive controller), and FLC (fuzzy logic controller), and the outcomes are compared in terms of settling time and steady-state error. The consequences characterize that the higher enactment of closed loop hybrid power flow controller (HPFC) 4-bus system with FLC UPFC controller’s (FL based) robustness is ensured from the simulation results as it has overcome the power quality issues like reactive power compensation, voltage sag mitigation, and THD reduction of transmission line current below 5% as per IEEE standard. Settling time of CL FBS is abridged from 0.87 to 0.62 sec, and steady-state error of voltage in CL FBS is abridged from 0.9 to 0.1 V using FLC.
34
Haghighat, Ezzatollah, Saeed Shaikhzadeh Najar, and Seyed Mohammad Etrati. "The Prediction of Needle Penetration Force in Woven Denim Fabrics Using Soft Computing Models." Journal of Engineered Fibers and Fabrics 9, no. 4 (2014): 155892501400900. http://dx.doi.org/10.1177/155892501400900406.
Full textAbstract:
The aim of this paper was to predict the needle penetration force in denim fabrics based on sewing parameters by using the fuzzy logic (FL) model. Moreover, the performance of fuzzy logic model is compared with that of the artificial neural network (ANN) model. The needle penetration force was measured on the Instron tensile tester. In order to plan the fuzzy logic model, the sewing needle size, number of fabric layers and fabric weight were taken into account as input parameters. The output parameter is needle penetration force. In addition, the same parameters and data are used in artificial neural network model. The results indicate that the needle penetration force can be predicted in terms of sewing parameters by using the fuzzy logic model. The difference between performance of fuzzy logic and neural network models is not meaningful ( RFL=0.971 and RANN=0.982). It is concluded that soft computing models such as fuzzy logic and artificial neural network can be utilized to forecast the needle penetration force in denim fabrics. Using the fuzzy logic model for predicting the needle penetration force in denim fabrics can help the garment manufacturer to acquire better knowledge about the sewing process. As a result, the sewing process may be improved, and also the quality of denim apparel increased.
35
Baalousha, Husam Musa, Bassam Tawabini, and Thomas D. Seers. "Fuzzy or Non-Fuzzy? A Comparison between Fuzzy Logic-Based Vulnerability Mapping and DRASTIC Approach Using a Numerical Model. A Case Study from Qatar." Water 13, no. 9 (2021): 1288. http://dx.doi.org/10.3390/w13091288.
Full textAbstract:
Vulnerability maps are useful for groundwater protection, water resources development, and land use management. The literature contains various approaches for intrinsic vulnerability assessment, and they mainly depend on hydrogeological settings and anthropogenic impacts. Most methods assign certain ratings and weights to each contributing factor to groundwater vulnerability. Fuzzy logic (FL) is an alternative artificial intelligence tool for overlay analysis, where spatial properties are fuzzified. Unlike the specific rating used in the weighted overlay-based vulnerability mapping methods, FL allows more flexibility through assigning a degree of contribution without specific boundaries for various classes. This study compares the results of DRASTIC vulnerability approach with the FL approach, applying both on Qatar aquifers. The comparison was checked and validated against a numerical model developed for the same study area, and the actual anthropogenic contamination load. Results show some similarities and differences between both approaches. While the coastal areas fall in the same category of high vulnerability in both cases, the FL approach shows greater variability than the DRASTIC approach and better matches with model results and contamination load. FL is probably better suited for vulnerability assessment than the weighted overlay methods.
36
Wang, Jianping, Qigao Feng, Jianwei Ma, and Yikun Feng. "FL-SDUAN: A Fuzzy Logic-Based Routing Scheme for Software-Defined Underwater Acoustic Networks." Applied Sciences 13, no. 2 (2023): 944. http://dx.doi.org/10.3390/app13020944.
Full textAbstract:
In underwater acoustic networks, the accurate estimation of routing weights is NP-hard due to the time-varying environment. Fuzzy logic is a powerful tool for dealing with vague problems. Software-defined networking (SDN) is a promising technology that enables flexible management by decoupling the data plane from the control plane. Inspired by this, we proposed a fuzzy logic-based software-defined routing scheme for underwater acoustic networks (FL-SDUAN). Specifically, we designed a software-defined underwater acoustic network architecture. Based on fuzzy path optimization (FPO-MST) and fuzzy cut-set optimization (FCO-MST), two minimum spanning tree algorithms under different network scales were proposed. In addition, we compared the proposed algorithms to state-of-the-art methods regarding packet delivery rate, end-to-end latency, and throughput in different underwater acoustic network scenarios. Extensive experiments demonstrated that a trade-off between performance and complexity was achieved in our work.
37
Lehal, Manpreet Singh. "Fuzzy in the Real World." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 7, no. 1 (2013): 473–77. http://dx.doi.org/10.24297/ijct.v7i1.3476.
Full textAbstract:
The Fuzzy Logic tool was introduced in 1965, by LotfiZadeh, and is a mathematical tool for dealing with uncertainty. It offers to a soft computing partnership the important concept of computing with words. It provides a technique to deal with imprecision and information granularity. Fuzzy Logic (FL) is a multi valued logic that allows intermediate values to be defined between conventional evaluations like true/false, yes/no, high/low, etc. Notions like rather tall or very fast can be formulated mathematically and processed by computers, in order to apply a more human like way of thinking in the programming of computers. Fuzzy Logic has emerged as a a profitable tool for the controlling and steering of systems and complex industrial processes, as well as for household and entertainment electronics, as well as for other expert systems and applications like the classification of SAR data.
38
Goswami, Ayush, and Mahesh Ram Patel. "A Review on Enhancing the properties of Stone Mastic Asphalt Using Bagasse and Coir Fiber as Additives." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 466–70. http://dx.doi.org/10.22214/ijraset.2023.49461.
Full textAbstract:
Abstract: Stone mastic asphalt is recognized for its remarkable durability, making it a crucial component for constructing pavements on aerital roads, which must withstand heavy traffic. When road authorities choose asphalt for main roads under these conditions, they often prefer stone mastic asphalt. The most crucial aspect of this type of asphalt is ensuring that SMA is implemented correctly, as it has excellent performance characteristics. However, improper implementation can lead to changes in performance. European countries favor SMA for its outstanding performance. There have been recent advancements in SMA methods, including computation and artificial intelligent systems such as artificial neural network and fuzzy logic (ANN and FL) in various engineering fields. It is vital to consider the resilient module when discussing fuzzy logic and SMA performance characteristics. Air voids, bulk density, and permeability coefficient are some of the critical SMA features that should be evaluated when applying fuzzy logic. In the initial stages, fuzzy logic utilizes weighted average operations to input data, and the output undergoes assessment by a mathematical model. Through experimental study, applying fuzzy logic can enhance the accuracy of evaluation.
39
Goswami, Ayush, and Mahesh Ram Patel. "Enhancing the Properties of Stone Mastic Asphalt Using Bagasse and Coir Fiber as Additives." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 452–65. http://dx.doi.org/10.22214/ijraset.2023.49460.
Full textAbstract:
Abstract: Stone mastic asphalt is recognized for its remarkable durability, making it a crucial component for constructing pavements on aerital roads, which must withstand heavy traffic. When road authorities choose asphalt for main roads under these conditions, they often prefer stone mastic asphalt. The most crucial aspect of this type of asphalt is ensuring that SMA is implemented correctly, as it has excellent performance characteristics. However, improper implementation can lead to changes in performance. European countries favor SMA for its outstanding performance. There have been recent advancements in SMA methods, including computation and artificial intelligent systems such as artificial neural network and fuzzy logic (ANN and FL) in various engineering fields. It is vital to consider the resilient module when discussing fuzzy logic and SMA performance characteristics. Air voids, bulk density, and permeability coefficient are some of the critical SMA features that should be evaluated when applying fuzzy logic. In the initial stages, fuzzy logic utilizes weighted average operations to input data, and the output undergoes assessment by a mathematical model. Through experimental study, applying fuzzy logic can enhance the accuracy of evaluation.
40
Surekha, B., Pandu R. Vundavilli, and M. B. Parappagoudar. "Reverse Modeling of Green Sand Mould System Using Fuzzy Logic-Based Approaches." Journal for Manufacturing Science & Production 12, no. 1 (2012): 1–16. http://dx.doi.org/10.1515/jmsp-2011-0012.
Full textAbstract:
AbstractIn the present study, reverse mapping problems of green sand mould system have been solved using Fuzzy Logic (FL)-based approaches. It is a complicated process, in which the quality of the castings is influenced by the mould properties (that is, green compression strength, permeability, hardness and others). In forward modeling, the outputs are expressed as the functions of input variables, whereas in reverse modeling, the later are represented as the functions of the former. The main advantage of reverse modeling lies in the fact that it helps in effective real-time control of the process. This paper proposes three different FL-based approaches for the reverse modeling of the green sand mould system. A binary-coded Genetic Algorithm (GA) has been used to optimize the knowledge base of the FL-based approaches, off-line. The developed approaches are found to solve the above problem effectively, and the performances of the developed approaches are compared among themselves. It has been observed that the approach “Automatic design of FL system using GA” yielded much better results in predicting a set of input variables from the set of known set of output.
41
Hussein, Shalau Farhad, Zena Ez Dallalbashi, and Ahmed Burhan Mohammed. "Anomaly detection in internet of medical things with artificial intillegence." Eastern-European Journal of Enterprise Technologies 1, no. 4 (121) (2023): 56–62. http://dx.doi.org/10.15587/1729-4061.2023.274575.
Full textAbstract:
Internet of things (IoT) becomes the most popular term in the recent advances in Healthcare devices. The healthcare data in the IoT process and structure is very sensitive and critical in terms of healthy and technical considerations. Outlier detection approaches are considered as principal tool or stage of any IoT system and are mainly categorized in statistical and probabilistic, clustering and classification-based outlier detection. Recently, fuzzy logic (FL) system is used in ensemble and cascade systems with other ML-based tools to enhance outlier detection performance but its limitation involves the false detection of outliers. In this paper, we propose a fuzzy logic system that uses the anomaly score of each point using local outlier factor (LOF), connectivity-based outlier factor (COF) and generalized LOF to eliminate the confusion in classifying points as outliers or inliers. Regarding human activity recognition (HAR) dataset, the FL achieved a value of 98.2 %. Compared to the performance of LOF, COF, and GLOF individually, the accuracy increased slightly, but the increase in precision and recall indicates an increase in correctly classified data and that neither true nor abnormal data is classified wrongly. The results show the increase in precision and recall which indicates an increase in correctly classified data. Thus, it can be confirmed that fuzzy logic with input of scores achieved the desired goal in terms of mitigating cases of false detection of anomalous data. By comparing the proposed ensemble of fuzzy logic and different types of local density scores in this study, the outcomes of fuzzy logic presents a new way of elaborating or fusing the different tools of the same purpose to enhance detection performance
42
Shalau, Farhad Hussein, Ez. Dallalbashi Zena, and Burhan Mohammed Ahmed. "Anomaly detection in internet of medical things with artificial intillegence." Eastern-European Journal of Enterprise Technologies 1, no. 4 (121) (2023): 56–62. https://doi.org/10.15587/1729-4061.2023.274575.
Full textAbstract:
Internet of things (IoT) becomes the most popular term in the recent advances in Healthcare devices. The healthcare data in the IoT process and structure is very sensitive and critical in terms of healthy and technical considerations. Outlier detection approaches are considered as principal tool or stage of any IoT system and are mainly categorized in statistical and probabilistic, clustering and classification-based outlier detection. Recently, fuzzy logic (FL) system is used in ensemble and cascade systems with other ML-based tools to enhance outlier detection performance but its limitation involves the false detection of outliers. In this paper, we propose a fuzzy logic system that uses the anomaly score of each point using local outlier factor (LOF), connectivity-based outlier factor (COF) and generalized LOF to eliminate the confusion in classifying points as outliers or inliers. Regarding human activity recognition (HAR) dataset, the FL achieved a value of 98.2 %. Compared to the performance of LOF, COF, and GLOF individually, the accuracy increased slightly, but the increase in precision and recall indicates an increase in correctly classified data and that neither true nor abnormal data is classified wrongly. The results show the increase in precision and recall which indicates an increase in correctly classified data. Thus, it can be confirmed that fuzzy logic with input of scores achieved the desired goal in terms of mitigating cases of false detection of anomalous data. By comparing the proposed ensemble of fuzzy logic and different types of local density scores in this study, the outcomes of fuzzy logic presents a new way of elaborating or fusing the different tools of the same purpose to enhance detection performance
43
Nasser, Khalid W., Salam J. Yaqoob, and Zainab A. Hassoun. "Improved dynamic performance of photovoltaic panel using fuzzy logic-MPPT algorithm." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (2021): 617–24. https://doi.org/10.11591/ijeecs.v21.i2.pp617-624.
Full textAbstract:
The nonlinear characteristics and intense credence dependence of photovoltaic (PV) panel on the solar irradiance and ambient temperature demonstrate important challenges for researchers in the PV panel topic. To overcome these problems, the maximum power point tracking (MPPT) controller is needed which can improve the PV panel efficiency. In other words, for maximum efficiency, the MPPT controller can help to extract the optimal and overall available output power from the PV panel at different output load conditions. Fuzzy logic (FL) is one of the strongest techniques in the extracting of MPP in the PV panel since it has several advantages; robust; no requirement to have an accurate mathematical model, and works with imprecise inputs. Therefore, in this paper, fuzzy logic (FL-MPPT) has been designed and simulated to improve dynamic performance PV panel at different solar irradiance and then increased the efficiency. Therefore, "MATLAB/Simulink software" has been used to build the proposed algorithm and the simulation results have been adequate as well. Besides, a robust FL-MPPT algorithm has been presented with high dynamic performance under different weather conditions. Finally, the proposed algorithm has a quicker response and less oscillatory comparison of the conventional algorithms in the subject of extracting the maximum PV power.
44
Redi, Mekonnen, Mihret Dananto, and Natesan Thillaigovindan. "A Bi-level Neuro-Fuzzy System Soft Computing for Reservoir Operation." International Journal of Advances in Soft Computing and its Applications 13, no. 3 (2021): 224–47. http://dx.doi.org/10.15849/ijasca.211128.15.
Full textAbstract:
Reservoir operation studies purely based on the storage level, inflow, and release decisions during dry periods only fail to serve the optimal reservoir operation policy design because of the fact that the release decision during this period is highly dependent on wet season water conservation and flood risk management operations. Imperatively, the operation logic in the two seasons are quite different. If the two operations are not sufficiently coordinated, they may produce poor responses to the system dynamics. There are high levels of uncertainties on the model parameters, values and how they are logically operated by human or automated systems. Soft computing methods represent the system as an artificial neural network (ANN) in which the input- output relations take the form of fuzzy numbers, fuzzy arithmetic and fuzzy logic (FL). Neuro-Fuzzy System (NFS) soft computing combine the approaches of FL and ANN for single purpose reservoir operation. Thus, this study proposes a Bi-Level Neuro-Fuzzy System (BL-NFS) soft computing methodology for short and long term operation policies for a newly inaugurated irrigation project in Gidabo Watershed of Main Ethiopian Rift Valley Basin. Keywords: Bankruptcy rule, BL-NFS, Reservoir operation, Sensitivity analysis, Soft computing, Water conservation.
45
Nagham, Hikmat Aziz, and Abdulrhman Al-Flaiyeh Maha. "Critical clearing time estimation of multi-machine power system transient stability using fuzzy logic." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (2023): 125–33. https://doi.org/10.11591/ijece.v13i1.pp125-133.
Full textAbstract:
Studying network stability requires determining the best critical clearing time (CCT) for the network after the fault has occurred. CCT is an essential issue for transient stability assessment (TSA) in the operation, security, and maintenance of an electrical power system. This paper proposes an algorithm to obtain CCT based on fuzzy logic (FL) under fault conditions, for a multi-machine power system. CCT was estimated using a two-step fuzzy logic algorithm: the first step is to calculate Δt, which represents the output of the FL, while maximum angle deviation (δmax) represents the input. The second step is to classify the system if it is a stable or unstable system, based on two inputs for FL, the first mechanical input power (Pm), the second average accelerations (Aav). The results of the proposed method were compared with the time domain simulation (TDS) method. The results showed the accuracy and speed of the estimation using the FL method, with an error rate not exceeding 5%, and reduced the performance time by about half the time. The proposed approach is tested on both IEEE-9 bus and IEEE-39 bus systems using simulation in MATLAB.
46
Hwang, Wen-Shyang, Teng-Yu Cheng, Yan-Jing Wu, and Ming-Hua Cheng. "Adaptive Handover Decision Using Fuzzy Logic for 5G Ultra-Dense Networks." Electronics 11, no. 20 (2022): 3278. http://dx.doi.org/10.3390/electronics11203278.
Full textAbstract:
With the explosive increase in traffic volume in fifth-generation (5G) mobile wireless networks, an ultra-dense network (UDN) architecture, composed of highly concentrated millimeter-wave base stations within the fourth-generation (4G) system, has been developed. User equipment (UE) may encounter more frequent handover opportunities when moving in a UDN. Conventional handover schemes are too simple to adapt to the diverse handover scenarios encountered in 5G UDNs because they consider only UE signal strength. Unnecessary handovers aggravate the ping-pong effect and degrade the quality of service of cellular networks. Fuzzy logic (FL) is considered the best technique to unravel the handover problem in a high-density scenario of small cells for 4G/5G networks. In this paper, we propose an FL-based handover scheme to dynamically adjust the values of two handover parameters, namely handover margin (HOM) and time to trigger (TTT), with respect to each UE. The proposed scheme, abbreviated as FLDHDT, has dynamic adjustment of TTT in addition to HOM by using the signal to interference plus noise ratio and horizontal moving speed of the UE as inputs to the FL controller. To demonstrate the effectiveness and superiority of FLDHDT, we perform simulations using the well-known ns-3 simulator. The performance measures include the number of handovers, overall system throughput, and ping-pong ratio. The simulation results demonstrate that FLDHDT improves the handover performance of 5G UDNs in terms of the number of handovers, ping-pong ratio, and overall system throughput compared to a conventional handover scheme, namely Event A3, and an FL-based handover scheme with dynamic adjustment of only HOM.
47
Kuk, Kristijan, Aleksandar Stanojević, Petar Čisar, et al. "Applications of Fuzzy Logic and Probabilistic Neural Networks in E-Service for Malware Detection." Axioms 13, no. 9 (2024): 624. http://dx.doi.org/10.3390/axioms13090624.
Full textAbstract:
The key point in the process of agent-based management in e-service for malware detection (according to accuracy criteria) is a decision-making process. To determine the optimal e-service for malware detection, two concepts were investigated: Fuzzy Logic (FL) and Probabilistic Neural Networks (PNN). In this study, three evolutionary variants of fuzzy partitioning, including regular, hierarchical fuzzy partitioning, and k-means, were used to automatically process the design of the fuzzy partition. Also, this study demonstrates the application of a feature selection method to reduce the dimensionality of the data by removing irrelevant features to create fuzzy logic in a dataset. The behaviors of malware are analyzed by fuzzifying relevant features for pattern recognition. The Apriori algorithm was applied to the fuzzified features to find the fuzzy-based rules, and these rules were used for predicting the output of malware detection e-services. Probabilistic neural networks were also used to find the ideal agent-based model for numerous classification problems. The numerical results show that the agent-based management performances trained with the clustering method achieve an accuracy of 100% with the PNN-MCD model. This is followed by the FL model, which classifies on the basis of linguistic variables and achieves an average accuracy of 82%.
48
W. Nasser, Khalid, Salam J. Yaqoob, and Zainab A. Hassoun. "Improved dynamic performance of photovoltaic panel using fuzzy Logic-MPPT algorithm." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (2021): 617. http://dx.doi.org/10.11591/ijeecs.v21.i2.pp617-624.
Full textAbstract:
The nonlinear characteristics and intense credence dependence of photovoltaic (PV) panel on the solar irradiance and ambient temperature demonstrate important challenges for researchers in the PV panel topic. To overcome these problems, the maximum power point tracking (MPPT) controller is needed which can improve the PV panel efficiency. In other words, for maximum efficiency, the MPPT controller can help to extract the optimal and overall available output power from the PV panel at different output load conditions. Fuzzy logic (FL) is one of the strongest techniques in the extracting of MPP in the PV panel since it has several advantages; robust; no requirement to have an accurate mathematical model, and works with imprecise inputs. Therefore, in this paper, fuzzy logic (FL-MPPT) has been designed and simulated to improve dynamic performance PV panel at different solar irradiance and then increased the efficiency. Therefore, "MATLAB/Simulink software" has been used to build the proposed algorithm and the simulation results have been adequate as well. Besides, a robust FL-MPPT algorithm has been presented with high dynamic performance under different weather conditions. Finally, the proposed algorithm has a quicker response and less oscillatory comparison of the conventional algorithms in the subject of extracting the maximum PV power.
49
Bonilla Huerta, Edmundo, Eduardo Martínez Juárez, Roberto Morales Caporal, and Eduardo Vázquez Urbina. "Fuzzy Logic and Machine Learning Algorithms for Detection and Classification of Falls and Activities of Daily Living." International Journal of Combinatorial Optimization Problems and Informatics 15, no. 4 (2024): 42–60. http://dx.doi.org/10.61467/2007.1558.2024.v15i4.497.
Full textAbstract:
This article analyses the movements of young and elderly people using data collected from an accelerometer and a gyroscope. This study proposes Type I fuzzy logic (FL) and several machine learning (ML) algorithms for the detection and classification of daily life movements and falls. The results obtained demonstrate that a fuzzy logic system can efficiently integrate data from an accelerometer and a gyroscope to classify falls and movements in daily life with 97.4% accuracy. When ML classifiers are used, the performance across several algorithms is also very high.
50
Sinha, Mukesh Kumar, and Rajesh Kumar Tiwari. "A Comprehensive Survey of Fuzzy Logic Utilization in Different Agricultural Sectors." Journal of Statistics and Mathematical Engineering 10, no. 1 (2024): 1–7. http://dx.doi.org/10.46610/josme.2024.v10i01.001.
Full textAbstract:
Fuzzy logic (FL) has emerged as a pivotal component within the realm of Expert Systems, demonstrating its efficacy in addressing real-life challenges that had previously eluded resolution. Its versatile applications span a multitude of domains, with this paper specifically delving into the successful utilization of fuzzy logic methods to tackle various agricultural issues. This comprehensive review explores instances where fuzzy logic has been seamlessly integrated into expert systems to provide innovative solutions within the field of agricultural sciences. The examined applications encompass a spectrum of challenges encountered in agriculture, showcasing the adaptability and effectiveness of fuzzy logic in addressing complex issues. This paper serves not only as an insightful examination of existing applications but also as a valuable contribution to the literature survey, laying the groundwork for future research endeavours. Particularly, it provides a foundational reference for those undertaking research aimed at developing expert systems tailored for specific crops in designated regions of our country. As a part of the broader landscape, this study acts as a cornerstone, offering a starting point for further investigations and advancements in the intersection of fuzzy logic and agricultural sciences.