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Journal articles on the topic 'Fuzzy logic (FL)'
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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.
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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.
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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 (November 5, 2022): 14564. http://dx.doi.org/10.3390/su142114564.
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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.
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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 (November 13, 2017): 470–87. http://dx.doi.org/10.1108/ijbpa-12-2016-0029.
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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.
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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 (May 10, 2020): 303–10. http://dx.doi.org/10.14710/ijred.9.2.303-310.
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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
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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 (April 11, 2021): 7011–17. http://dx.doi.org/10.48084/etasr.4031.
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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.
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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.
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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.
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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 (April 2021): 003685042110143. http://dx.doi.org/10.1177/00368504211014346.
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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.
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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 (October 21, 2021): 2665. http://dx.doi.org/10.3390/math9212665.
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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.
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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.
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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.
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Thakur, Amey. "Neuro-Fuzzy: Artificial Neural Networks & Fuzzy Logic." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 128–35. http://dx.doi.org/10.22214/ijraset.2021.37930.
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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.
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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.
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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.
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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 (December 2020): 30902. http://dx.doi.org/10.1051/epjap/2020200189.
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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.
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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 (February 1, 2020): 265. http://dx.doi.org/10.11591/ijece.v10i1.pp265-274.
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<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>
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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 (June 5, 2019): 767. http://dx.doi.org/10.3390/sym11060767.
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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).
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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 (May 2018): 155014771877848. http://dx.doi.org/10.1177/1550147718778482.
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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.
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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 (March 1, 2017): 1. http://dx.doi.org/10.11591/ijai.v6.i1.pp1-7.
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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.
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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 (May 1, 2021): 1288. http://dx.doi.org/10.3390/w13091288.
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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.
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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.
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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.
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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 (January 1, 2022): 1–23. http://dx.doi.org/10.4018/ijcini.314782.
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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.
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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 (January 29, 2019): 561. http://dx.doi.org/10.3390/s19030561.
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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.
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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 (February 12, 2022): 1333. http://dx.doi.org/10.3390/en15041333.
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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.
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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 (April 1, 2012): 1–16. http://dx.doi.org/10.1515/jmsp-2011-0012.
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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.
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Lehal, Manpreet Singh. "Fuzzy in the Real World." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 7, no. 1 (May 30, 2013): 473–77. http://dx.doi.org/10.24297/ijct.v7i1.3476.
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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.
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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 (October 12, 2022): 3278. http://dx.doi.org/10.3390/electronics11203278.
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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.
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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 (January 10, 2023): 944. http://dx.doi.org/10.3390/app13020944.
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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.
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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 (December 2014): 155892501400900. http://dx.doi.org/10.1177/155892501400900406.
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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.
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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 (February 1, 2021): 617. http://dx.doi.org/10.11591/ijeecs.v21.i2.pp617-624.
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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.
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Aziz, Nagham Hikmat, and Maha Abdulrhman Al-Flaiyeh. "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 (February 1, 2023): 125. http://dx.doi.org/10.11591/ijece.v13i1.pp125-133.
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<span lang="EN-US">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.</span>
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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 (November 28, 2021): 224–47. http://dx.doi.org/10.15849/ijasca.211128.15.
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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.
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Bengag, Asmae, Amina Bengag, and Omar Moussaoui. "Intrusion detection based on fuzzy logic for wireless body area networks: review and proposition." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (May 1, 2022): 1091. http://dx.doi.org/10.11591/ijeecs.v26.i2.pp1091-1102.
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Wireless body area networks (WBANs) are very helpful for monitoring the patient’s case, due to the medical sensors. However, this technology faces several problems such as loss communication, security issues and energy consumption. Our work focused on the security and specifically the intrusion detection system (IDS), which is one of the most effective techniques used to identify the presence of intrusions in a network. To make the IDS more efficient, the fuzzy logic (FL) is one of the well-known techniques that is known for its powerful mechanism used to differentiate network traffic levels. In this paper, we start to present an overview of IDS and FL functionality. Moreover, we give a survey of recent works dealing IDS based on FL in wireless sensor and classify them on different measures. Hence, our comparative study is very helpful for the researchers, to understand the use of FL in IDS and have clear vision for developing their own security solution. In the second part, we develop a novel IDS based on Mamdani type fuzzy inference system for detecting jamming attacks in WBAN. Our IDS was built in Matlab, also we are used Castalia platform and OMNET++ simulator to simulate different scenarios of WBAN.
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Bhuvaneswari, V. "Microarray Gene Expression Analysis Using Type 2 Fuzzy Logic (Mga-Fl)." International Journal of Computer Science, Engineering and Applications 2, no. 2 (April 30, 2012): 53–69. http://dx.doi.org/10.5121/ijcsea.2012.2205.
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Khanum, Afshan, S. Purushothaman, and P. Rajeswari. "Performance comparisons of the soft computing algorithms in lung segmentation and nodule identification." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 189. http://dx.doi.org/10.14419/ijet.v7i1.1.9287.
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This paper presents the implementation back propagation algorithm (BPA) and fuzzy logic(FL) in lung image segmentation and nodule identification. Lung image database consortium (LIDC) database images has been used. Features are extracted using statistical methods. These features are used for training the BPA and FL algorithms. Weights are stored in a file that is used for segmentation of the lung image. Subsequently, texture properties are used for nodule identification.
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Douiri, Moulay Rachid, Ouissam Belghazi, and Mohamed Cherkaoui. "Recurrent Self-Tuning Neuro-Fuzzy for Speed Induction Motor Drive." Journal of Circuits, Systems and Computers 24, no. 09 (August 27, 2015): 1550131. http://dx.doi.org/10.1142/s0218126615501315.
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This paper proposes a hybrid recurrent neuro-fuzzy (RNF) architecture for rotor speed regulation of indirect field oriented controlled (IFOC) induction motor (IM) drive. This approach incorporates Takagi–Sugeno–Kang (TSK) model-based fuzzy logic (FL) laws with a four-layer artificial neural networks (ANNs) scheme. Moreover, for the proposed RNF an improved self-tuning method is developed based on the IM theory and its high performance requirements. The principal task of the tuning method is to adjust the parameters of the FL in order to minimize the square of the error between actual and reference output. The convergence/divergence of the weights is discussed and investigated by simulation.
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Gharde, K. D., Mahesh Kothari, and D. M. Mahale. "Forecasting Runoff and Sediment Yield by ANN and Fuzzy Logic Algorithms for Kal River, India." Current World Environment 11, no. 3 (December 25, 2016): 892–906. http://dx.doi.org/10.12944/cwe.11.3.25.
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The ANN and fuzzy logic (FL) models were developed to forecast the runoff and sediment yield for catchment of Kal River, India in METLAB 2.9b witting the programme supporting to nntool. The input to the models were used as daily rainfall, evaporation, temperature and one day and tow day lag runoff for runoff modelling. Whereas, for sediment yield modelling inputs in ANN and Fuzzy logic model used as daily rainfall, one and two day runoff. The inputs data for both models of 21 years (1991 to 2011) were considered in present study on daily basis. The 14 years (1991 to 2004) used in developing the models whereas rest 7 years (2005 to 2011) for validation of the models. In sediment yield modelling, 7 years (2003 to 2009) data were used for developing and validation of models. The models performance were evaluated by standard statistical indices such R, RMSE, EV, CE, and MAD. It was found that ANN model performance improved with increasing the input vectors. The fuzzy logic model was performed well with R value more than 0.95 during developmental stage and validation stage over ANN model for predicting runoff and sediment yield. Hence, FL model found to be more superior to ANN in prediction of runoff and sediment yield for Kal river.
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Kanirajan, P., M. Joly, and T. Eswaran. "Recognition of Power Quality Disturbances using Fuzzy Expert Systems." WSEAS TRANSACTIONS ON SIGNAL PROCESSING 16 (January 19, 2021): 166–77. http://dx.doi.org/10.37394/232014.2020.16.18.
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This paper presents a new approach to detect and classify power quality disturbances in the power system using Fuzzy C-means clustering, Fuzzy logic (FL) and Radial basis Function Neural Networks (RBFNN). Feature extracted through wavelet is used for training, after training, the obtained weight is used to classify the power quality problems in RBFNN, but it suffers from extensive computation and low convergence speed. Then to detect and classify the events, FL is proposed, the extracted characters are used to find out membership functions and fuzzy rules being determined from the power quality inherence. For the classification,5 types of disturbance are taken in to account. The classification performance of FL is compared with RBFNN.The clustering analysis is used to group the data in to clusters to identifying the class of the data with Fuzzy C-means algorithm. The classification accuracy of FL and Fuzzy C-means clustering is improved with the help of cognitive as well as the social behavior of particles along with fitness value using Particle swarm optimization (PSO),just by determining the ranges of the feature of the membership funtion for each rules to identify each disturbance specifically.The simulation result using Fuzzy C-means clustering possess significant improvements and gives classification results in less than a cycle when compared over other considered approach.
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Kanirajan, P., and M. Joly. "Fuzzy Expert System for Recognition of Power Quality Disturbances." WSEAS TRANSACTIONS ON ELECTRONICS 11 (May 20, 2020): 60–71. http://dx.doi.org/10.37394/232017.2020.11.8.
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This paper presents a new approach to detect and classify power quality disturbances in the power system using Fuzzy C-means clustering, Fuzzy logic (FL) and Radial basis Function Neural Networks (RBFNN). Feature extracted through wavelet is used for training, after training, the obtained weight is used to classify the power quality problems in RBFNN, but it suffers from extensive computation and low convergence speed. Then to detect and classify the events, FL is proposed, the extracted characters are used to find out membership functions and fuzzy rules being determined from the power quality inherence. For the classification,5 types of disturbance are taken in to account. The classification performance of FL is compared with RBFNN.The clustering analysis is used to group the data in to clusters to identifying the class of the data with Fuzzy C-means algorithm. The classification accuracy of FL and Fuzzy C-means clustering is improved with the help of cognitive as well as the social behavior of particles along with fitness value using Particle swarm optimization (PSO),just by determining the ranges of the feature of the membership funtion for each rules to identify each disturbance specifically.The simulation result using Fuzzy C-means clustering possess significant improvements and gives classification results in less than a cycle when compared over other considered approach.
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Lei, Tao, Yanbo Wang, Xianqiu Jin, Zhihao Min, Xingyu Zhang, and Xiaobin Zhang. "An Optimal Fuzzy Logic-Based Energy Management Strategy for a Fuel Cell/Battery Hybrid Power Unmanned Aerial Vehicle." Aerospace 9, no. 2 (February 21, 2022): 115. http://dx.doi.org/10.3390/aerospace9020115.
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With the development of high-altitude and long-endurance unmanned aerial vehicles (UAVs), optimization of the coordinated energy dispatch of UAVs’ energy management systems has become a key target in the research of electric UAVs. Several different energy management strategies are proposed herein for improving the overall efficiency and fuel economy of fuel cell/battery hybrid electric power systems (HEPS) of UAVs. A rule-based (RB) energy management strategy is designed as a baseline for comparison with other strategies. An energy management strategy (EMS) based on fuzzy logic (FL) for HEPS is presented. Compared with classical rule-based strategies, the fuzzy logic control has better robustness to power fluctuations in the UAV. However, the proposed FL strategy has an inherent defect: the optimization performances will be determined by the heuristic method and the past experiences of designers to a great extent rather than a specific cost function of the algorithm itself. Thus, the paper puts forward an improved fuzzy logic-based strategy that uses particle swarm optimization (PSO) to track the optimal thresholds of membership functions, and the equivalent hydrogen consumption minimization is considered as the objective function. Using a typical 30 min UAV mission profile, all the proposed EMS were verified by simulations and rapid controller prototype (RCP) experiments. Comprehensive comparisons and analysis are presented by evaluating hydrogen consumption, system efficiency and voltage bus stability. The results show that the PSO-FL algorithm can further improve fuel economy and achieve superior overall dynamic performance when controlling a UAV’s fuel-cell powertrain.
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Mashhadany (SMIEEE), Yousif Al, Sameh Jassam, and Elaf Hamzah Yahia. "Design and Simulation of Modified Type-2 Fuzzy Logic Controller for Power System." International Journal of Electrical and Electronics Research 10, no. 3 (September 30, 2022): 731–36. http://dx.doi.org/10.37391/ijeer.100352.
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This Article exhibits the structure of a Modified Type-2 (MT2) Fuzzy Logic (FL) Controller (MT2FLC), direction line programming of development, also performance optimization for different power systems. The implementation of the MT2FLC for control of a power system. New participation capacities were considered in adjusting a domain for an Interval Type-2 (IT2) Fuzzy Logic (FL) System (IT2FLS). Another structure in graphic user interface (GUI) mimicked four controllers: an optimal PID controller, FLC, a Type-1(TIFLC), an Interval Type-2 ((IT2FLC), and the MIT2FLC. Their yields were analysed, different periods of the structure procedure for the fuzzy framework, from beginning depiction to conclusive execution, can be gotten from the altered tool compartment (whose capacity to create complex frameworks and adaptability in broadening the accessible usefulness into working with adjusted type2 fuzzy administrators, phonetic factors, IT2 participation capacities, and defuzzification strategies, just as in assessing the MIT2FLC are its best characteristics). Case study for this work, all the optimization controllers implemented for a Brushless DC (BLDC) Motor with MATLAB Ver.2012a was utilized in the recreation and plan of the entirety of the procedure GUIs. Satisfactory results are obtaining which improve the implementation of the using of MIT2FLC controller as practical solution of power system.
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Molleti, Venkat Pankaj Lahari, Rama Sudha Kashibhatla, and Vijaya Santhi Rajamahanthi. "MPPT Control of Photovoltaic Systems Using Statechart With Abstraction and Its Comparison With Fuzzy Logic." International Journal of System Dynamics Applications 11, no. 6 (September 1, 2021): 1–14. http://dx.doi.org/10.4018/ijsda.20221101.oa1.
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Maximum power point tracking(MPPT) control is an indispensable aspect of photovoltaic(PV) systems. Many MPPT techniques including a few based on soft-computing have been employed earlier. The soft-computing techniques include fuzzy-FSMs (Finite State Machines)which are integration of fuzzy logic (FL) into states or transitions of FSMs which are used for control and modeling of real-time systems. However, FSMs pose certain disadvantages as compared to its advanced variant called ‘statecharts’. In this work, statecharts with abstraction layers are proposed for MPPT control of PV system. An abstract-statechart MPPT(ASM) controller is developed and is verified with PV system using co-simulation. A C++ based FL MPPT program is also developed, which is independent of any predefined and simulation-only functions. A conceptual estimation of execution time of such a FL MPPT program is presented and compared with the execution times delivered by proposed ASM controller. It can be observed that the ASM controller gives accurate, fast tracking speeds, along with the advantage of abstraction.
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Guo, Na, Caihong Li, Tengteng Gao, Guoming Liu, Yongdi Li, and Di Wang. "A Fusion Method of Local Path Planning for Mobile Robots Based on LSTM Neural Network and Reinforcement Learning." Mathematical Problems in Engineering 2021 (June 12, 2021): 1–21. http://dx.doi.org/10.1155/2021/5524232.
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Due to the limitation of mobile robots’ understanding of the environment in local path planning tasks, the problems of local deadlock and path redundancy during planning exist in unknown and complex environments. In this paper, a novel algorithm based on the combination of a long short-term memory (LSTM) neural network, fuzzy logic control, and reinforcement learning is proposed, and uses the advantages of each algorithm to overcome the other’s shortcomings. First, a neural network model including LSTM units is designed for local path planning. Second, a low-dimensional input fuzzy logic control (FL) algorithm is used to collect training data, and a network model (LSTM_FT) is pretrained by transferring the learned method to learn the basic ability. Then, reinforcement learning is combined to learn new rules from the environments autonomously to better suit different scenarios. Finally, the fusion algorithm LSTM_FTR is simulated in static and dynamic environments, and compared to FL and LSTM_FT algorithms, respectively. Numerical simulations show that, compared to FL, LSTM_FTR can significantly improve decision-making efficiency, improve the success rate of path planning, and optimize the path length. Compared to the LSTM_FT, LSTM_FTR can improve the success rate and learn new rules.
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Krzywanski, Jaroslaw, Marta Wesolowska, Artur Blaszczuk, Anna Majchrzak, Maciej Komorowski, and Wojciech Nowak. "Fuzzy logic and bed-to-wall heat transfer in a large-scale CFBC." International Journal of Numerical Methods for Heat & Fluid Flow 28, no. 1 (January 2, 2018): 254–66. http://dx.doi.org/10.1108/hff-09-2017-0357.
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Purpose The purpose of this paper is to first present the key features of the fuzzy logic (FL) approach as a cost-effective technique in simulations of complex systems and then demonstrate the formulation and application of the method. Design/methodology/approach The FL approach is used as an alternative method of data handling, considering the complexity of analytical and numerical procedures and high costs of empirical experiments. The distance from gas distributor, the temperature and the voidage of the bed, flue gas velocity and the load of the boiler are the input parameters, whereas the overall heat transfer coefficient for the membrane walls constitutes the output. Five overlapping sigmoid and constant linguistic terms are used to describe the input and the output data, respectively. The Takagi–Sugeno inference engine and the weighted average defuzzification methods are applied to determine the fuzzy and crisp output value, respectively. Findings The performed FL model allows predicting the bed-to-wall heat transfer coefficient in a large-scale 670 t/h circulating fluidized bed (CFB) boiler. The local heat transfer coefficients evaluated using the developed model are in very good agreement with the data obtained in complementary investigations. Originality/value The performed model constitutes an easy-to-use and functional tool. The new approach can be helpful for further research on the bed-to-wall heat transfer coefficient in the CFB units.
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Shahid, Muhammad Arslan, Ghulam Abbas, Mohammad Rashid Hussain, Muhammad Usman Asad, Umar Farooq, Jason Gu, Valentina E. Balas, Muhammad Uzair, Ahmed Bilal Awan, and Tanveer Yazdan. "Artificial Intelligence-Based Controller for DC-DC Flyback Converter." Applied Sciences 9, no. 23 (November 26, 2019): 5108. http://dx.doi.org/10.3390/app9235108.
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This paper presents an intelligent voltage controller designed on the basis of an adaptive neuro-fuzzy inference system (ANFIS) for a flyback converter (FC) working in continuous conduction mode (CCM). The union of fuzzy logic (FL) and adaptive neural networks (ANN) makes ANFIS more robust against model parameters’ uncertainties and perturbations in input voltage or load current. ANFIS inherits the advantages of structured knowledge representation from FL and learning capability from NN. Comparative analysis showed that the ANFIS controller offers not only the superior transient response characteristics, but also excellent steady-state characteristics compared to those of the FL controller (FLC) and proportional–integral–derivative (PID) controllers, thus validating its superiority over these traditional controllers. For this purpose, MATLAB/Simulink environment-based simulation results are presented for validation of the proposed converter compensated system under all operating conditions.
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Laribi, M. A., A. Mlika, L. Romdhane, and S. Zeghloul. "A combined genetic algorithm–fuzzy logic method (GA–FL) in mechanisms synthesis." Mechanism and Machine Theory 39, no. 7 (July 2004): 717–35. http://dx.doi.org/10.1016/j.mechmachtheory.2004.02.004.
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Veerasamy, B., and C. M. Sangeetha. "MB-FL: Macro-Block Fuzzy Logic for Video Compression in Multimedia Applications." INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS 22, no. 4 (December 31, 2022): 366–72. http://dx.doi.org/10.5391/ijfis.2022.22.4.366.
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Üneş, Fatih, Mustafa Demirci, Martina Zelenakova, Mustafa Çalışıcı, Bestami Taşar, František Vranay, and Yunus Ziya Kaya. "River Flow Estimation Using Artificial Intelligence and Fuzzy Techniques." Water 12, no. 9 (August 29, 2020): 2427. http://dx.doi.org/10.3390/w12092427.
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Accurate determination of river flows and variations is used for the efficient use of water resources, the planning of construction of water structures, and preventing flood disasters. However, accurate flow prediction is related to a good understanding of the hydrological and meteorological characteristics of the river basin. In this study, flow in the river was estimated using Multi Linear Regression (MLR), Artificial Neural Network (ANN), M5 Decision Tree (M5T), Adaptive Neuro-Fuzzy Inference System (ANFIS), Mamdani-Fuzzy Logic (M-FL) and Simple Membership Functions and Fuzzy Rules Generation Technique (SMRGT) models. The Stilwater River in the Sterling region of the USA was selected as the study area and the data obtained from this region were used. Daily rainfall, river flow, and water temperature data were used as input data in all models. In the paper, the performance of the methods is evaluated based on the statistical approach. The results obtained from the generated models were compared with the recorded values. The correlation coefficient (R), Mean Square Error (MSE), and Mean Absolute Error (MAE) statistics are computed separately for each model. According to the comparison criteria, as a final result, it is considered that Mamdani-Fuzzy Logic (M-FL) and Simple Membership Functions and Fuzzy Rules Generation Technique (SMRGT) model have better performance in river flow estimation than the other models.
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Chaudhary, Gopal, Amena .., and J. A. Lobo Marques. "Sentiment Analysis from Social Media Tweets Using Single-Valued Neutrosophic Sets and Fuzzy Sets." Journal of Neutrosophic and Fuzzy Systems 5, no. 2 (2023): 51–59. http://dx.doi.org/10.54216/jnfs.050205.
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In the last ten years, exciting work at the intersection of several academic disciplines has been done in the areas of view mining and sentiment analysis. The sheer amount of social media text that is now accessible for sentiment analysis has expanded by a factor of multiples with the development of social media networks, resulting in the creation of a formidable corpus. An examination of the sentiments included within tweets has been performed to measure the general public's perspective on breaking news, as well as a variety of laws, regulations, individuals, and political movements. In the assessment of the sentiment of Twitter data, fuzzy logic (FL) was used, but neutrosophy, which makes consideration the idea of indeterminacy, was not applied. Fuzzy logic (FL) was utilized since neutrosophy was not utilized to analyze tweets. In this study, we present the idea of single valued-neutrosophic sets (SVNSs) that may have positive, indeterminate, and negative memberships. We used the sanders dataset to apply the proposed methodology. The fuzzy set (FS) has the indeterminacy value opposite the NS. FS has two only degrees, truth, and falsity. This paper shows the difference between the NS and FS in the sample of data.
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Ko, Chien-Ho. "Predicting Subcontractor Performance Using Web-Based Evolutionary Fuzzy Neural Networks." Scientific World Journal 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/729525.
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Subcontractor performance directly affects project success. The use of inappropriate subcontractors may result in individual work delays, cost overruns, and quality defects throughout the project. This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NNs). FL is primarily used to mimic high level of decision-making processes and deal with uncertainty in the construction industry. NNs are used to identify the association between previous performance and future status when predicting subcontractor performance. GAs are optimizing parameters required in FL and NNs. EFNNs encode FL and NNs using floating numbers to shorten the length of a string. A multi-cut-point crossover operator is used to explore the parameter and retain solution legality. Finally, the applicability of the proposed EFNNs is validated using real subcontractors. The EFNNs are evolved using 22 historical patterns and tested using 12 unseen cases. Application results show that the proposed EFNNs surpass FL and NNs in predicting subcontractor performance. The proposed approach improves prediction accuracy and reduces the effort required to predict subcontractor performance, providing field operators with web-based remote access to a reliable, scientific prediction mechanism.
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Kissara, Wafaa, and Baydaa Hassan. "Determination of Fetal sex by Fetal anatomy parameters using a Fuzzy C-Mean Cluster." Al-Kitab Journal for Pure Sciences 05, no. 02 (December 27, 2021): 9–25. http://dx.doi.org/10.32441/kjps.05.02.p2.
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This paper proposes a new approach to determining the sex of the fetus using the measurement of dimensions of the head. The research attempted to use one of the techniques of fuzzy logic in the field of medicine, and here it was dealt with the visual properties designed to mix the properties of fuzzy logic (FL) and feature images. The results that some traits cannot give good results such as the results obtained from the local binary pattern (LBP) algorithm and the power and superiority of the results of hybrid filters because the ultrasound images have a special color spectrum. The results also showed the ability of the fuzzy logic proposed by using the characteristics derived from the hybrid filter to deal with the study of images and to achieve a better diagnosis of the gender of the fetus through measuring the dimensions of the head.
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Daneshpayeh, Sajjad, Amir Tarighat, Faramarz Ashenai Ghasemi, and Mohammad Sadegh Bagheri. "A fuzzy logic model for prediction of tensile properties of epoxy/glass fiber/silica nanocomposites." Journal of Elastomers & Plastics 50, no. 6 (October 18, 2017): 491–500. http://dx.doi.org/10.1177/0095244317733768.
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The object of this work is to study and predict the tensile properties (tensile strength, elastic modulus, and elongation at break) of ternary nanocomposites based on epoxy/glass fiber/nanosilica using the fuzzy logic (FL). Two factors in three levels including glass fiber at 0, 5, and 10 wt% and nanosilica at 0, 0.5, and 1 wt% were chosen for adding to an epoxy matrix. From FL surfaces, it was found that the glass fiber content had a main role in the tensile properties of nanocomposites. The high levels of glass fiber content led to a significant increase in the elastic modulus and generally, the presence of glass fiber decreased the tensile strength and elongation at break. Also, addition of the nanosilica content resulted in an increased elastic modulus but decreased the elongation at break of nanocomposites. Finally, an FL model was obtained for each tensile property.
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Ramasamy, Viswanathan, Jagatheswari Srirangan, and Praveen Ramalingam. "Fuzzy and Position Particle Swarm Optimized Routing in VANET." International journal of electrical and computer engineering systems 12, no. 4 (November 26, 2021): 199–206. http://dx.doi.org/10.32985/ijeces.12.4.3.
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In Intelligent Transport Systems, traffic management and providing stable routing paths between vehicles using vehicular ad hoc networks (VANET's) is critical. Lots of research and several routing techniques providing a long path lifetime have been presented to resolve this issue. However, the routing algorithms suffer excessive overhead or collisions when solving complex optimization problems. In order to improve the routing efficiency and performance in the existing schemes, a Position Particle Swarm Optimization based on Fuzzy Logic (PPSO-FL) method is presented for VANET that provides a high-quality path for communication between nodes. The PPSO-FL has two main steps. The first step is selecting candidate nodes through collectively coordinated metrics using the fuzzy logic technique, improving packet delivery fraction, and minimizing end-to-end delay. The second step is the construction of an optimized routing model. The optimized routing model establishes an optimal route through the candidate nodes using position-based particle swarm optimization. The proposed work is simulated using an NS2 simulator. Simulation results demonstrate that the method outperforms the standard routing algorithms in packet delivery fraction, end-to-end delay and execution time for routing in VANET scenarios.