Relevant bibliographies by topics / Fuzzy logic (FL)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Fuzzy logic (FL)'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 June 2025

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Journal articles on the topic "Fuzzy logic (FL)"

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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.

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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.
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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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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.

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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.
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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 (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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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 (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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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.

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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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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.

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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.
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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.

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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.
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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 (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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Dissertations / Theses on the topic "Fuzzy logic (FL)"

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Li, Zhi. "Fuzzy logic based robust control of queue management and optimal treatment of traffic over TCP/IP networks." University of Southern Queensland, Faculty of Sciences, 2005. http://eprints.usq.edu.au/archive/00001461/.

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Improving network performance in terms of efficiency, fairness in the bandwidth, and system stability has been a research issue for decades. Current Internet traffic control maintains sophistication in end TCPs but simplicity in routers. In each router, incoming packets queue up in a buffer for transmission until the buffer is full, and then the packets are dropped. This router queue management strategy is referred to as Drop Tail. End TCPs eventually detect packet losses and slow down their sending rates to ease congestion in the network. This way, the aggregate sending rate converges to the network capacity. In the past, Drop Tail has been adopted in most routers in the Internet due to its simplicity of implementation and practicability with light traffic loads. However Drop Tail, with heavy-loaded traffic, causes not only high loss rate and low network throughput, but also long packet delay and lengthy congestion conditions. To address these problems, active queue management (AQM) has been proposed with the idea of proactively and selectively dropping packets before an output buffer is full. The essence of AQM is to drop packets in such a way that the congestion avoidance strategy of TCP works most effectively. Significant efforts in developing AQM have been made since random early detection (RED), the first prominent AQM other than Drop Tail, was introduced in 1993. Although various AQMs also tend to improve fairness in bandwidth among flows, the vulnerability of short-lived flows persists due to the conservative nature of TCP. It has been revealed that short-lived flows take up traffic with a relatively small percentage of bytes but in a large number of flows. From the user’s point of view, there is an expectation of timely delivery of short-lived flows. Our approach is to apply artificial intelligence technologies, particularly fuzzy logic (FL), to address these two issues: an effective AQM scheme, and preferential treatment for short-lived flows. Inspired by the success of FL in the robust control of nonlinear complex systems, our hypothesis is that the Internet is one of the most complex systems and FL can be applied to it. First of all, state of the art AQM schemes outperform Drop Tail, but their performance is not consistent under different network scenarios. Research reveals that this inconsistency is due to the selection of congestion indicators. Most existing AQM schemes are reliant on queue length, input rate, and extreme events occurring in the routers, such as a full queue and an empty queue. This drawback might be overcome by introducing an indicator which takes account of not only input traffic but also queue occupancy for early congestion notification. The congestion indicator chosen in this research is traffic load factor. Traffic load factor is in fact dimensionless and thus independent of link capacity, and also it is easy to use in more complex networks where different traffic classes coexist. The traffic load indicator is a descriptive measure of the complex communication network, and is well suited for use in FL control theory. Based on the traffic load indicator, AQM using FL – or FLAQM – is explored and two FLAQM algorithms are proposed. Secondly, a mice and elephants (ME) strategy is proposed for addressing the problem of the vulnerability of short-lived flows. The idea behind ME is to treat short-lived flows preferably over bulk flows. ME’s operational location is chosen at user premise gateways, where surplus processing resources are available compared to other places. By giving absolute priority to short-lived flows, both short and long-lived flows can benefit. One problem with ME is starvation of elephants or long-lived flows. This issue is addressed by dynamically adjusting the threshold distinguishing between mice and elephants with the guarantee that minimum capacity is maintained for elephants. The method used to dynamically adjust the threshold is to apply FL. FLAQM is deployed to control the elephant queue with consideration of capacity usage of mice packets. In addition, flow states in a ME router are periodically updated to maintain the data storage. The application of the traffic load factor for early congestion notification and the ME strategy have been evaluated via extensive experimental simulations with a range of traffic load conditions. The results show that the proposed two FLAQM algorithms outperform some well-known AQM schemes in all the investigated network circumstances in terms of both user-centric measures and network-centric measures. The ME strategy, with the use of FLAQM to control long-lived flow queues, improves not only the performance of short-lived flows but also the overall performance of the network without disadvantaging long-lived flows.
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Windridge, David, Michael Felsberg, and Affan Shaukat. "A Framework for Hierarchical Perception–Action Learning Utilizing Fuzzy Reasoning." Linköpings universitet, Datorseende, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-85688.

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Perception-action (P-A) learning is an approach to cognitive system building that seeks to reduce the complexity associated with conventional environment-representation/action-planning approaches. Instead, actions are directly mapped onto the perceptual transitions that they bring about, eliminating the need for intermediate representation and significantly reducing training requirements. We here set out a very general learning framework for cognitive systems in which online learning of the P-A mapping may be conducted within a symbolic processing context, so that complex contextual reasoning can influence the P-A mapping. In utilizing a variational calculus approach to define a suitable objective function, the P-A mapping can be treated as an online learning problem via gradient descent using partial derivatives. Our central theoretical result is to demonstrate top-down modulation of low-level perceptual confidences via the Jacobian of the higher levels of a subsumptive P-A hierarchy. Thus, the separation of the Jacobian as a multiplying factor between levels within the objective function naturally enables the integration of abstract symbolic manipulation in the form of fuzzy deductive logic into the P-A mapping learning. We experimentally demonstrate that the resulting framework achieves significantly better accuracy than using P-A learning without top-down modulation. We also demonstrate that it permits novel forms of context-dependent multilevel P-A mapping, applying the mechanism in the context of an intelligent driver assistance system.<br>DIPLECS<br>GARNICS<br>CUAS
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Branco, Hermes Manoel Galvão Castelo. "Uma estratégia para a detecção e classificação de transitórios em transformadores de potência pela utilização da transformada Wavelet e da lógica Fuzzy." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/18/18154/tde-09092009-083518/.

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Nesta pesquisa, apresentam-se os principais eventos relacionados com a proteção de transformadores e sua correlação com os distúrbios de qualidade da energia elétrica (QEE). Neste sentido, foi desenvolvido um algoritmo que utiliza a transformada Wavelet (TW) e a lógica Fuzzy (LF) para classificar os eventos transitórios associados à proteção de transformadores. Estes eventos foram observados em um sistema elétrico de potência (SEP) simulado com a utilização do software Alternative Transients Program (ATP). Importa ressaltar que o sistema modelado apresenta transformadores ligados em paralelo, possibilitando o estudo de eventos decorrentes desta situação, como a energização solidária (Sympathetic Inrush). Por este SEP, modelado sobre parâmetros reais, foram simuladas várias situações transitórias, que provocam o aparecimento de correntes diferenciais, sendo estas direcionadas para análise do algoritmo desenvolvido. Afirma-se que, nos testes realizados, o algoritmo proposto apresentou um desempenho satisfatório perante as mais variadas situações a que foi submetido, identificando as causas das correntes diferenciais, sejam proporcionadas por defeitos ou por outras condições de operação aplicadas.<br>In this research, the main events related to the transformer protection and its correlation with the power quality disturbances (PQ) are presented. In this context, an algorithm based on Wavelet transform (WT) and Fuzzy logic (FL) was developed to classify the transient events associated with the transformer protection. These events were observed in an electrical power system (EPS) simulated using the Alternative Transients Program (ATP) software. It should be emphasized that the modeled system presents transformers connected in parallel, allowing the study of events of this situation, such as sympathetic inrush. For the simulated EPS, modeled based on real parameters, various transients situationswere simulated, causing the appearance of differentials currents which were directed to the analysis. The proposed algorithm showed a satisfactory performance tomany situations, identifying the causes of the differentials currents, either provided by faults or other operation conditions.
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(12971010), Masoud Mohammadian. "Integrating neural networks, fuzzy logic and genetic algorithms for intelligent control." Thesis, 1993. https://figshare.com/articles/thesis/Integrating_neural_networks_fuzzy_logic_and_genetic_algorithms_for_intelligent_control/20174402.

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<p>In this thesis, the integration of Neural Networks (NNs), Fuzzy Logic (FL) and Genetic Algorithms (GAs) for intelligent control is proposed and applied to the classical problem of docking a truck.</p> <p>A backpropagation neural network architecture using a "step" update weight modification is used to obtain quickly and efficiently trajectory data from given initial states. A new algorithm to define fuzzy logic rules is used on the trajectory data to build a fuzzy logic knowledge base. This fuzzy logic knowledge base is then optimised using a genetic algorithm to obtain a fuzzy logic controller that effectively simulates a full neural network solution to the problem of docking of a truck.</p>
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Books on the topic "Fuzzy logic (FL)"

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Belohlavek, Radim, Joseph W. Dauben, and George J. Klir. Fuzzy Logic and Mathematics. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190200015.001.0001.

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The term “fuzzy logic” (FL) is a generic one, which stands for a broad variety of logical systems. Their common ground is the rejection of the most fundamental principle of classical logic—the principle of bivalence—according to which each declarative sentence has exactly two possible truth values—true and false. Each logical system subsumed under FL allows for additional, intermediary truth values, which are interpreted as degrees of truth. These systems are distinguished from one another by the set of truth degrees employed, its algebraic structure, truth functions chosen for logical connectives, and other properties. The book examines from the historical perspective two areas of research on fuzzy logic known as fuzzy logic in the narrow sense (FLN) and fuzzy logic in the broad sense (FLB), which have distinct research agendas. The agenda of FLN is the development of propositional, predicate, and other fuzzy logic calculi. The agenda of FLB is to emulate commonsense human reasoning in natural language and other unique capabilities of human beings. In addition to FL, the book also examines mathematics based on FL. One chapter in the book is devoted to overviewing successful applications of FL and the associated mathematics in various areas of human affairs. The principal aim of the book is to assess the significance of FL and especially its significance for mathematics. For this purpose, the notions of paradigms and paradigm shifts in science, mathematics, and other areas are introduced and employed as useful metaphors.
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Book chapters on the topic "Fuzzy logic (FL)"

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Heister, Hanns-Werner. "Einleitung. Vier Prinzipien und fünf Erscheinungsformen der FL im Musikprozess." In Musik und Fuzzy Logic. Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-662-63006-8_1.

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Zadeh, Lotfi A. "Some reflections on the relationship between AI and fuzzy logic (FL) —A heretical view." In Fuzzy Logic in Artificial Intelligence. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/bfb0095067.

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Khanna, Daksh, Tanupriya Choudhury, A. Sai Sabitha, and Nguyen Gia Nhu. "Microarray Gene Expression Analysis Using Fuzzy Logic (MGA-FL)." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1951-8_16.

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Wu, Yining. "Transforming Fuzzy Description Logic $\mathcal{ALC}_\mathcal{FL}$ into Classical Description Logic $\mathcal{ALCH}$." In Uncertainty Reasoning for the Semantic Web II. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35975-0_10.

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Mandal, Sujit, and Subrata Mondal. "Knowledge-Driven Statistical Approach for Landslide Susceptibility Assessment Using GIS and Fuzzy Logic (FL) Approach." In Statistical Approaches for Landslide Susceptibility Assessment and Prediction. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93897-4_7.

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Fathima Shemim, K. S., and Ulf Witkowski. "Enhanced Energy-Efficient Fuzzy Logic Clustering and Network Coding Strategy for Wireless Sensor Networks (EEE-FL-NC)." In Advanced Computing and Intelligent Technologies. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2164-2_5.

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Zindani, Divya, Apurba Kumar Roy, Kaushik Kumar, and J. Paulo Davim. "Fuzzy Logic for Machining Applications." In Advanced Fuzzy Logic Approaches in Engineering Science. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5709-8.ch016.

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There have been umpteen research reports on the usage of artificial intelligence (AI) strategies for modelling various machining processes. One of the well-known AI strategies is that of fuzzy logic (FL) techniques that has been used for prediction of machining performance variables for both the categories of machining processes and controls the machining process. Given the increasing trend of FL in machining, the chapter reviews the application of fuzzy logic in modelling and controlling the machining processes. The work begins with introduction section and then proceeds to discuss the importance role played by FL strategies in the traditional and modern manufacturing processes. The work summarizes some of the major applications of FL-based systems in various machining processes. Limitations, advantages, and the improvements to minimize the limitations are then discussed. The authors of the chapter hope that the review will aid all those researching in the domain of manufacturing sciences and their optimization techniques.
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Martínez-Barrera, Gonzalo, Osman Gencel, Ahmet Beycioglu, Serkan Subaşı, and Nelly González-Rivas. "Artificial Intelligence Methods and Their Applications in Civil Engineering." In Fuzzy Systems. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1908-9.ch059.

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Simulation of material properties generally involves the development of a mathematical model derived from experimental data. In structural mechanics and construction materials contexts, recent experiments have reported that fuzzy logic (FL), artificial neural networks (ANNs), genetic algorithm (GA), and fuzzy genetic (FG) may offer a promising alternative. They are known as artificial intelligence (AI). In civil engineering, AI methods have been extensively used in the fields of civil engineering applications such as construction management, building materials, hydraulic, optimization, geotechnical and transportation engineering. Many studies have examined the applicability of AI methods to estimate concrete properties. This chapter described the principles of FL methods that can be taught to engineering students through MATLAB graphical user interface carried out in a postgraduate course on Applications of Artificial Intelligence in Engineering, discussed the application of Mamdani type in concrete technology and highlighted key studies related to the usability of FL in concrete technology.
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Kolomvatsos, Kostas, and Stathes Hadjiefthymiades. "On the Use of Fuzzy Logic in Electronic Marketplaces." In Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-61350-429-1.ch030.

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Today, there is a large number of product providers in the Web. Electronic Marketplaces (EMs) enable entities to negotiate and trade products. Usually, intelligent agents assume the responsibility of representing buyers or sellers in EMs. However, uncertainty about the characteristics and intentions of the negotiating entities is present in these scenarios. Fuzzy Logic (FL) theory presents a lot of advantages when used in environments where entities have limited or no knowledge about their peers. Hence, entities can rely on a FL knowledge base that determines the appropriate action on every possible state. FL can be used in offers, trust, or time constraints definition or when an agent should decide during the negotiation process. The autonomic nature of agents in combination with FL leads to more efficient systems. In this chapter, the authors provide a critical review on the adoption of FL in marketplace systems and present their proposal for the buyer side. Moreover, the authors describe techniques for building FL systems focusing on clustering techniques. The aim is to show the importance of FL adoption in such settings.
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Banimelhem, Omar, Eyad Taqieddin, Moad Y. Mowafi, Fahed Awad, and Feda' Al-Ma'aqbeh. "Fuzzy Logic-Based Cluster Heads Percentage Calculation for Improving the Performance of the LEACH Protocol." In Fuzzy Systems. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1908-9.ch027.

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In wireless sensor networks, cluster-based routing was proven to be the most energy-efficient strategy to deal with the scaling problem. In addition, selecting the proper number of clusters is a critical decision that can impose a significant impact on the energy consumption and the network lifetime. This paper presents FL-LEACH, a variant of the well-known LEACH clustering protocol, which attempts to relax the stringent strategy of determining the number of clusters used by LEACH via fuzzy logic decision-making scheme. This relates the number of clusters to a number of network characteristics such as the number of sensor nodes, the area of the sensing field, and the location of the base station. The performance of FL-LEACH was evaluated via simulation and was compared against LEACH using standard metrics such as network lifetime and remaining network energy. The results depicted that the proposed approach has the potential to substantially conserve the sensor node energy and extend lifetime of the network.
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Conference papers on the topic "Fuzzy logic (FL)"

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Attaullah, Hasina, Sanaullah Sanaullah, and Thorsten Jungeblut. "FL-DL: Fuzzy Logic with Deep Learning, Hybrid Anomaly Detection and Activity Prediction in Smart Homes Data-Sets." In 2024 IEEE 24th International Symposium on Computational Intelligence and Informatics (CINTI). IEEE, 2024. https://doi.org/10.1109/cinti63048.2024.10830825.

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Tayal, Shikha, Taskeen Zaidi, and Preeti Gera. "Utilization of Support Vector Machines (SVM), Fuzzy Logic (FL) & Adaptive Neuro-Fuzzy Inference System (ANFIS) for Carrying Out Proficient Energy Routing in 5G Wireless Networks." In 2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI). IEEE, 2024. https://doi.org/10.1109/icscai61790.2024.10866528.

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Gannamneni, Nanda Kishore, Archit Joshi, Priyank Mohan, Ramya Ramachandran, Krishan Kumar, and Shakeb Khan. "QPSO-FL-EECP: Quantum Particle Swarm Optimization and Fuzzy Logic-Based Protocol for Energy-Efficient Clustering Protocol in Wireless Sensor Networks." In 2024 13th International Conference on System Modeling & Advancement in Research Trends (SMART). IEEE, 2024. https://doi.org/10.1109/smart63812.2024.10882515.

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Zadeh, Lotfi A. "Fuzzy logic: principles, applications, and perspectives." In Orlando '91, Orlando, FL, edited by Mohan M. Trivedi. SPIE, 1991. http://dx.doi.org/10.1117/12.45456.

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Priebe, Russell, and Richard A. Jones. "Fuzzy logic approach to multitarget tracking in clutter." In Orlando '91, Orlando, FL, edited by Michael K. Masten and Larry A. Stockum. SPIE, 1991. http://dx.doi.org/10.1117/12.45703.

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Crawford, Anthony L., and Dean B. Edwards. "Implementing Fuzzy Logic in the Control of a Biologically Inspired Robotic Cat Leg." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49785.

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This research discusses the implementation of a fuzzy logic control system to drive the movement of a simplified cat leg model. The system’s movement in this paper addresses a planar motion where the model experiences a fixed horizontal velocity and a harmonic vertical displacement. The fuzzy logic (FL) controller applies membership functions to fuzzify the position and velocity errors and applies height defuzzification to generate the time dependant forcing function for the system’s horizontal and vertical governing equations. A PID controller is also applied as a benchmark for this research. Both controllers are optimized using the simplex method for which the FL controller performed just as well as the PID controller with more promise of accounting for the nonlinear influences that were neglected in this simplified cat leg model and requiring actuators with a lower required force range. This research provides the skeletal structure for which an effective total controller can be built on.
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Bello, Opeyemi, Francisco Serdio Fernández, Catalin Teodoriu, Joachim Oppelt, Javier Holzmann, and Susanne Saminger-Platz. "Novel Application of Fuzzy Logic (FL) Method for Drilling Rig Selection." In Abu Dhabi International Petroleum Exhibition and Conference. Society of Petroleum Engineers, 2015. http://dx.doi.org/10.2118/177653-ms.

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Basehore, Paul, and Joseph T. Yestrebsky. "Innovative architectural and theoretical considerations yield efficient fuzzy logic controller VLSI design." In Orlando '91, Orlando, FL, edited by Vibeke Libby. SPIE, 1991. http://dx.doi.org/10.1117/12.44850.

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Karr, Charles L. "Design of a cart-pole balancing fuzzy logic controller using a genetic algorithm." In Orlando '91, Orlando, FL, edited by Mohan M. Trivedi. SPIE, 1991. http://dx.doi.org/10.1117/12.45446.

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Laribi, M. A., L. Romdhane, A. Mlika, and S. Zeghloul. "A Combined Genetic Algorithm-Fuzzy Logic Method (GA-FL) to Design a 6 Bars Planar Mechanism." In ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58193.

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This work deals with solution methods of optimal synthesis of planar mechanisms. A searching procedure presents a combined genetic algorithm–fuzzy logic method to solve the problem of path generation in mechanism synthesis. Previous works, dealing with the same problem and using the genetic algorithm method, suffered from the lack of precision, especially for large domain problems. The proposed method is made of a classical genetic algorithm coupled with a fuzzy logic controller (GA-FL). This controller monitors the variation of the design variables during the first run of the genetic algorithm and modifies the initial bounding intervals to restart a second round of the genetic algorithm. For both of these runs, we limited the number of generations to roughly half of the number found in the literature, without reducing the accuracy of the final solution. Compared to previous works on the same problem, our method proved to be more efficient in finding the optimal mechanism. The effectiveness of the proposed method has been demonstrated on a six bars synthesis example.
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