Relevant bibliographies by topics / Fuzzy logic decision

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

Academic literature on the topic 'Fuzzy logic decision'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Fuzzy logic decision"

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Zaliluddin, Dadan. "Bibliometric Analysis of “Accuracy of Multi Criteria Decision Making (MCDM) of Assistance Recipients with Fuzzy Logic Algorithm”." West Science Interdisciplinary Studies 1, no. 07 (2023): 329–39. http://dx.doi.org/10.58812/wsis.v1i07.82.

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The issue of making decisions accurately and swiftly is crucial in the present and is bolstered by an abundance of data; therefore, making correct decisions can save the future. With a large amount of data and numbers, however, the decision-making process will become even more muddled if the statistical ranking values are identical. Therefore, a method is required to determine whether a hazy decision becomes clearer or a decision that is nearly identical is the best. The method used has existed for more than 50 years, and it is fuzzy logic. In the selection of fuzzi, the term Multi-Criteria Decision Making (MCDM) is frequently used, and it continues to be used and expanded. As a result, the increasing number of articles that contain information about Fuzzy Logic Multi-Criteria Decision Making (MCDM) can be used as research material using Bibliometric analysis based on the Scopus. With Bibliometric analysis, tens of thousands of related articles can be analyzed and displayed with VOSviewer software using a variety of categories including authors, titles, citations, updates, and other information to demonstrate the most recent direction of future research on fuzzy logic Multi Criteria Decision Making (MCDM).
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Ulten, Taylan Ozgur, and Omer Faruk BAY. "TEWA Interface with Fuzzy Logic." International Journal of Engineering Research in Computer Science and Engineering 9, no. 4 (2022): 4–7. http://dx.doi.org/10.36647/ijercse/09.04.art002.

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It is an important task to determine the degree of threat of threats in military applications and in the war environment. Small mistakes that can be made during the threat evaluation phase can cause great loss of life and property. Mistakes that can be made at this stage can lead to serious consequences that are difficult to compensate. For this reason, it is of great importance to accurately calculate the threat degree of the detected aircraft. For this purpose, computer decision support systems provide significant support to the operators in the decision-making process in order to increase the cognitive ability of the operator and to implement the decisions taken quickly. In this study, a decision support system is designed to contribute to the decision-making processes of the operators. In order to perform calculations in the decision support system; A threat assessment interface was created with the help of C# programming language using Microsoft Visual Studio. In the study, calculations were made using the speed, altitude and distance parameters of an aircraft classified as a threat to be used in the threat evaluation and weapon assignment interface. Fuzzy logic method was used to calculate the threat degree.
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C, Swathi, Jenifer Ebienazer, Swathi M, and Suruthipriya S. "Fuzzy Logic." International Journal of Innovative Research in Information Security 09, no. 03 (2023): 147–52. http://dx.doi.org/10.26562/ijiris.2023.v0903.19.

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Fuzzy logic is a mathematical framework for reasoning about ambiguous or inaccurate information. It is founded on the idea that truth can be stated as a degree of membership in a fuzzy set rather than as a binary value of true or untrue. Fuzzy logic is used in control systems, artificial intelligence, and decision-making. This paper defines fuzzy logic and discusses its key concepts, mathematical underpinnings, and applications. We look at the benefits and drawbacks.
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Nur, Akbar. "The Effect of Neighboring Cells on Handover Decision Making Based on Fuzzy in the WCDMA Network." Jurnal Jartel: Jurnal Jaringan Telekomunikasi 5, no. 2 (2017): 52–58. http://dx.doi.org/10.33795/jartel.v5i2.205.

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In channel transfer (handover) from one Base Station to another Base Station. The purpose of this final project is to analyze the effect of neighboring cells on handover decisions on WCDMA networks based on fuzzy, in this handover process, handover decisions use several parameters related to handovers and supported by fuzzy logic. Relatively high user mobility demands a guarantee until the use of the service ends, the impact of user mobility results in the output being analyzed for this handover decision to help give consideration to the optimal handover decision. The method used is Tsukamoto fuzzy logic, for decision making, while the measurement method In the field, the drive test method is carried out by measuring the signal level around the base station area, and comparing the results of the two methods. Comparison of handover decisions between the results of fuzzy logic and measurements, for example for the results of no proper in fuzzy logic, yields a rate value of 0% for soft handovers and 100% for hard handovers, and for proper results in fuzzy logic, yields a rate value for measurement. 95.22% for soft / soft handover and 4.72% for hard handover
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Druzhynin, V. A., M. M. Stepanov, G. B. Zhyrov, and L. O. Rіaba. "ALGORITHM FOR USING FUZZY LOGIC IN MANAGEMENT AND DECISION-MAKING MODELS." Collection of scientific works of the Military Institute of Kyiv National Taras Shevchenko University, no. 69 (2020): 75–81. http://dx.doi.org/10.17721/2519-481x/2020/69-08.

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In real conditions, when the task of formally describing the control process of a rather complex process arises, it is necessary to take into account several external factors (parameters) and their values, which potentially tend to Infinity. At the same time, the system's response is not limited to just one control action. To automate the process of composing all possible combinations of linguistic descriptions of variables at the stage of fuzzy conditional statements and the decision-making mechanism on the use of control actions in the development of control and decision-making models, it is proposed to use fuzzy logical models. Ways to construct algorithms for converting input perturbations of complex systems into conceptual relations for automating the control process and supporting decision-making are considered. The fuzzy logic apparatus relation is used to formalize, process, and make decisions about the use of system control signals in response to external disturbances. Fuzzy control systems combine information from human experts (natural language) with measurements and mathematical models. Fuzzy Systems will turn the knowledge base into a mathematical formulation that has proven very effective in many applications. When designing a fuzzy system, many questions need to be answered, in particular in creating linguistic models to describe the functioning of complex systems, in particular radar mapping systems with recognition of objects on the ground and making decisions for controlling unmanned systems. Thus, at the stage of composing a set of fuzzy instructions (statements), it is of interest to formalize the following processes, such as determining all possible combinations of terms of linguistic variables and making a decision on the application of control actions, depending on external factors. In the process of formalizing the process of determining all possible combinations and terms of linguistic variables, it is necessary to create fuzzy instructions (rules) for managing a system or object for fuzzy-logical control models and decision-making in the process of developing models for the functioning of complex systems.
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Seen, Xie Shone, Darvishi Mondragon Ortiz-Barrios, and Osei Scott Kant. "A novel stochastic fuzzy decision model for optimizing decision-making in the manufacturing industry." International Journal of Enterprise Modelling 17, no. 1 (2023): 15–23. http://dx.doi.org/10.35335/emod.v17i1.69.

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In unpredictable and imprecise production environments, this research introduces a stochastic fuzzy decision model for the manufacturing industry. Decision-makers can use the stochastic and fuzzy logic model to capture uncertainties, variability, and language representations of industrial factors. The choice problem, fuzzy input variables, and crisp outcome variables are identified to start the research. Linguistic terms related with fuzzy input variables are represented by fuzzy sets and membership functions. Fuzzy rules link fuzzy input variables to crisp output variables based on expert knowledge or historical data. Objective function, restrictions, and fuzzy rules are incorporated into the stochastic fuzzy decision model's mathematical formulation. Decision-makers can maximize outcomes by considering stochastic factors and fuzzy logic with the model. The model uses an optimization technique to find the optimal choice variable values. A numerical example of manufacturing production planning illustrates the model's use. The results show that the stochastic fuzzy decision model may minimize production costs by calculating optimal production quantities depending on demand. The research concludes that the proposed approach helps manufacturing companies make decisions. Decision-makers can use the model to make educated judgments despite uncertainties and inaccurate information. Future study will explore additional aspects and integrate the model into decision support systems or industrial software. In dynamic and uncertain manufacturing contexts, the stochastic fuzzy decision model empowers manufacturing decision-makers to make optimal decisions
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Valaskova, Katarina, Viera Bartosova, and Pavol Kubala. "Behavioural Aspects of the Financial Decision-Making." Organizacija 52, no. 1 (2019): 22–31. http://dx.doi.org/10.2478/orga-2019-0003.

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Abstract Background and Purpose: Behavioural finance is a relatively new, but rapidly evolving field that provides explanations of an economic decision-making by cognitive psychology, conventional economic and financial theory. Behavioural finance searches the influence of psychology on the behaviour of financial practitioners and the subsequent effects on the financial markets. The purpose of the paper is the research on behavioural aspects of financial decision-making as they help explain why and how markets might be inefficient. Design/Methodology/Approach: Fuzzy logic is an excellent tool for working with linguistic variables that are often found when working with behavioural data. Thus, we analyse the financial decision-making process from the perspective of behavioural finance aimed at better understanding of the decision-making process of investors applying the principles of fuzzy logic to solve various financial problems. Results: The results of the study indicate that fuzzy logic is applicable when solving problems of financial management and financial decision-making problems. The urgency of the fuzzy logic application for managerial and financial decisions should be emphasized. Research in this area indicates that in some cases, as in the case of behavioural financing, the use of fuzzy logic is far more suitable than the use of other methods (Peters, Aguiar and Sales). Conclusion: The novelty of the paper is to extend the application of fuzzy sets in the area of financial decision-making. The paper demonstrates that despite the fact, that fuzzy logic is currently used mainly in technical directions, it is applicable also in financial management, especially, in cases where it is necessary to consider the influence of human and the occurrence of linguistic variables.
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Çağlayan, Nihan, Sina Abbasi, İbrahim Yilmaz, and Babek Erdebilli. "Bibliometric Analysis on the Distributed Decision, Decentralized Decision, and Fuzzy Logic." Discrete Dynamics in Nature and Society 2024 (January 25, 2024): 1–13. http://dx.doi.org/10.1155/2024/7305880.

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This study aims to survey the bibliometric properties of distributed decisions, decentralized decisions, and fuzzy articles published between 1995 and 2023 in the Web of Science (WoS) database. During the analysis process, the keywords “distributed decision, decentralized decision, and fuzzy” were scanned in all languages, both in the titles and the content of all publication types. As a result of the analysis, 79 articles in all fields comprised the dataset. The most used keywords in the articles were related to the distributed decision, decentralized decision, and fuzzy logic, and the most frequently cited publications were examined using the social network analysis method, which uses VOSviewer (version 1.6.19) to visualize the relationships. The study’s goal on “active researchers, active journals, journal metrics, title document type, active countries, and active institutions” was to look at the words most frequently used in articles on distributed, decentralized, and fuzzy logic. The social network analysis represented the relationships between these keywords and the most frequently cited publications. The findings demonstrated a significant correlation between using these keywords in academic literature and their contribution to this field’s research. These results can assist researchers in finding potential partners and keeping up with current research trends. Overall, this study offers important new perspectives on the state of research on fuzzy logic, distributed decision making, and decentralized decision making.
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Sharma, Manoj. "Fuzzy Logic Based Handover Decision System." International Journal of Ad hoc, Sensor & Ubiquitous Computing 3, no. 4 (2012): 21–29. http://dx.doi.org/10.5121/ijasuc.2012.3403.

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Popchev, Vania Peneva, Ivan. "FUZZY LOGIC OPERATORS IN DECISION-MAKING." Cybernetics and Systems 30, no. 8 (1999): 725–45. http://dx.doi.org/10.1080/019697299124966.

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Dissertations / Theses on the topic "Fuzzy logic decision"

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Levy, Bat-Sheva. "Fuzzy logic, a model to explain students' mathematical decision-making." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0026/MQ51391.pdf.

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Hinojosa, William. "Probabilistic fuzzy logic framework in reinforcement learning for decision making." Thesis, University of Salford, 2010. http://usir.salford.ac.uk/26716/.

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This dissertation focuses on the problem of uncertainty handling during learning by agents dealing in stochastic environments by means of reinforcement learning. Most previous investigations in reinforcement learning have proposed algorithms to deal with the learning performance issues but neglecting the uncertainty present in stochastic environments. Reinforcement learning is a valuable learning method when a system requires a selection of actions whose consequences emerge over long periods for which input-output data are not available. In most combinations of fuzzy systems with reinforcement learning, the environment is considered deterministic. However, for many cases, the consequence of an action may be uncertain or stochastic in nature. This work proposes a novel reinforcement learning approach combined with the universal function approximation capability of fuzzy systems within a probabilistic fuzzy logic theory framework, where the information from the environment is not interpreted in a deterministic way as in classic approaches but rather, in a statistical way that considers a probability distribution of long term consequences. The generalized probabilistic fuzzy reinforcement learning (GPFRL) method, presented in this dissertation, is a modified version of the actor-critic learning architecture where the learning is enhanced by the introduction of a probability measure into the learning structure where an incremental gradient descent weight- updating algorithm provides convergence. XXIABSTRACT Experiments were performed on simulated and real environments based on a travel planning spoken dialogue system. Experimental results provided evidence to support the following claims: first, the GPFRL have shown a robust performance when used in control optimization tasks. Second, its learning speed outperforms most of other similar methods. Third, GPFRL agents are feasible and promising for the design of adaptive behaviour robotics systems.
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Ribeiro, Maria Rita Sarmento de Almeida. "Application of support logic theory to fuzzy multiple attribute decision problems." Thesis, University of Bristol, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.357891.

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Strasser, Mark. "The development of a fuzzy decision-support system for dairy cattle culling decisions." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ29794.pdf.

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Mitchell, Sophia. "A Cascading Fuzzy Logic Approach for Decision Making in Dynamic Applications." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1448037866.

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Almejalli, Khaled A., Keshav P. Dahal, and M. Alamgir Hossain. "Intelligent traffic control decision support system." Springer-Verlag, 2007. http://hdl.handle.net/10454/2554.

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When non-recurrent road traffic congestion happens, the operator of the traffic control centre has to select the most appropriate traffic control measure or combination of measures in a short time to manage the traffic network. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control measures that need to be considered during the decision making process. The identification of suitable control measures for a given non-recurrent traffic congestion can be tough even for experienced operators. Therefore, simulation models are used in many cases. However, simulating different traffic scenarios for a number of control measures in a complicated situation is very time-consuming. In this paper we propose an intelligent traffic control decision support system (ITC-DSS) to assist the human operator of the traffic control centre to manage online the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural network, and genetic algorithm. These approaches form a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a GA algorithm for identifying fuzzy rules, and the back-propagation neural network algorithm for fine tuning the system parameters. The proposed system has been tested for a case-study of a small section of the ring-road around Riyadh city. The results obtained for the case study are promising and show that the proposed approach can provide an effective support for online traffic control.
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Naim, Nur Syibrah Muhamad. "A type-2 fuzzy logic approach for multi-criteria group decision making." Thesis, University of Essex, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.635990.

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Multi-Criteria Group Decision Making (MCGDM) is a decision tool which is able to find a unique agreement from a group of decision makers (DMs) by evaluating various conflicting criteria. However, the current multi-criteria decision making with a group of DMs (MCGDM) techniques do not effectively deal with the large number of possibilities that cause disagreement between different judgements and the variety of ideas and opinions among the decision makers which lead to high_uncertainty levels. There is a growing interest to investigate techniques to handle the faced uncertainties in many decision making applications. Studies in fuzzy decision making have grown rapidly in the utilisation of extended fuzzy set theories (i.e., Type-2 Fuzzy Sets, Intuitionistic Fuzzy Sets, Hesitant Fuzzy Sets, Vague Sets, Interval-valued Fuzzy Sets; etc.) to evaluate the faced uncertainties.
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Sequeira, Movin. "Developing decision-support tools for evaluation of manufacturing reshoring decisions." Licentiate thesis, Tekniska Högskolan, Jönköping University, JTH, Industriell produktutveckling, produktion och design, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48263.

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During last three decades, companies have offshored their manufacturing activities across international borders in order to pursue lower manufacturing costs. Despite having accomplished their purpose, companies have also suffered from issues, especially poor quality of products and a poor response to customer demand. Therefore, companies consider relocating some of the manufacturing activities back to the home country, a process that is known as manufacturing reshoring. There is paucity of scholarly attention on how manufacturing reshoring decisions are evaluated and supported. Therefore, the purpose of this thesis is to develop decision-support tools to evaluate manufacturing reshoring decisions. In order to fulfil this, it is important to know how industry experts reason while making manufacturing reshoring decisions (RQ1), and how their reasoning can be modeled into decision-support tools (RQ2). Therefore, three studies were conducted including a multiple case study and two modeling studies. The multiple case study addressed the criteria that are considered by the industry experts in these decisions, while the two modeling studies, based on fuzzy logic and analytical hierarchy process (AHP), used a part of these criteria to develop decision-support tools. The findings indicate that a holistic set of criteria were considered by industry experts in arriving at a manufacturing reshoring decision. A large portion of these criteria occur within competitive priority category and among them, high importance is given to quality, while low importance to sustainability. Fuzzy logic modeling was used to model the criteria from the perspective of competitive priority at an overall level. Three fuzzy logic concepts were developed to capture industry experts’ reasoning and facilitate modeling of manufacturing reshoring decisions. Furthermore, two configurations and sixteen settings were developed, of which, the best ones were identified. AHP-based tools were used to capture experts’ reasoning of the competitive priority criteria by comparing the criteria. It was observed that fuzzy logic-based tools are able to better emulate industry experts’ reasoning of manufacturing reshoring. This research contributes to theory with a holistic framework of reshoring decision criteria, and to practice with decision-support tools for evaluation of manufacturing reshoring decisions.<br>Under de tre senaste decennierna har många företag flyttat sin produktion till lågkostnadsländer för att kunna utnyttja lägre lönekostnader. Många gånger har företagen genom denna åtgärd lyckats sänka sin tillverkningskostnad men samtidigt drabbats av oförutsedda problem kopplat till exempelvis produkt-kvalitet och möjligheten att kundanpassa produkter. Hanteringen av problemen har lett till ytterligare kostnader som många gånger överstigit besparingen i tillverkningskostnad. Detta har lett till att allt fler företag börjat flytta tillbaka sin produktion till hemlandet, så kallad reshoring. Reshoring är ett ungt område där det saknas forskning gällande bland annat hur den här typen av beslut på bästa sätt kan utvärderas och vilken typ av beslutstöd som kan underlätta den här typen av beslut. Därför är syftet med den här avhandlingen är att utveckla beslutsstödverktyg för utvärdering av reshoring beslut. För att uppfylla syftet har två forskningsfrågor formulerats. Den första frågan handlar om hur industriexperter resonerar kring reshoring beslut (RQ1) medan den andra frågan handlar om hur deras resonemang kan modelleras i beslutsstödverktyg (RQ2). Tre studier har genomförts för att besvara forskningsfrågorna, en fallstudie och två modelleringsstudier. Fallstudien fokuserar på att identifiera vilka kriterier som industriexperter beaktar medan modelleringsstudierna fokuserar på att utveckla beslutstödsverktyg där en del av dessa kriterier beaktas, med hjälp av fuzzy logic och analytical hierarchy process (AHP). Resultaten från forskningen visar att industriexperter bedömer reshoring beslut utifrån ett holistiskt perspektiv. En stor del av dessa beslutskriterier finns inom konkurrenskraft kategorin och inom dessa, har industriexperterna lagt högst vikt på kvalitet och lägst vikt på hållbarhet. Genom fuzzy logic modellering modellerades kriterierna på en övergripande nivå. Tre nya fuzzy logic koncept utvecklades för att fånga experternas resonemang. Dessutom utvecklades två konfigurationer med sexton olika inställningar, och de bästa identifierades. AHP-baserade verktyg utvecklades för att fånga experternas resonemang om kriterierna för konkurrenskraft prioriteringar. Fuzzy logic-baserade verktyg kan bättre fånga experternas resonemang kring reshoring beslut. Denna forskning bidrar till teori med en holistisk lista över beslutskriterier för reshoring beslut, och till praktik med beslutsstöd verktyg för utvärdering av reshoring beslut.
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Yang, Junli. "A decision support system for material routing in construction sites." Thesis, University of Wolverhampton, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366041.

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Wang, Ming-hua. "A knowledge-based system approach for project management decision-making support." Thesis, University of Warwick, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340476.

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Books on the topic "Fuzzy logic decision"

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Guidara, Ali. Policy Decision Modeling with Fuzzy Logic. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62628-0.

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Lootsma, Freerk A. Fuzzy Logic for Planning and Decision Making. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4757-2618-3.

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Lootsma, Freerk A. Fuzzy logic for planning and decision making. Kluwer Academic Publishers, 1997.

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Lootsma, Freerk A. Fuzzy Logic for Planning and Decision Making. Springer US, 1997.

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United States. National Aeronautics and Space Administration., ed. Multi-objective decision-making under uncertainty: Fuzzy logic methods. National Aeronautics and Space Administration, 1994.

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M, Gupta Madan, and Yamakawa Takeshi 1946-, eds. Fuzzy logic in knowledge-based systems, decision and control. North-Holland, 1988.

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United States. National Aeronautics and Space Administration., ed. Multi-objective decision-making under uncertainty: Fuzzy logic methods. National Aeronautics and Space Administration, 1994.

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Maria, Bojadziev, ed. Fuzzy logic for business, finance, and management. 2nd ed. World Scientific, 2007.

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Maria, Bojadziev, ed. Fuzzy logic for business, finance, and management. World Scientific, 1997.

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Lösungsverfahren für mehrkriterielle Entscheidungsprobleme: Klassische Verfahren, neuronale Netze und Fuzzy Logic. Lang, 1998.

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Book chapters on the topic "Fuzzy logic decision"

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Bandyopadhyay, Susmita. "Fuzzy Theory and Fuzzy Logic." In Decision Support System. CRC Press, 2023. http://dx.doi.org/10.1201/9781003307655-5.

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Khazaii, Javad. "Fuzzy Logic." In Advanced Decision Making for HVAC Engineers. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33328-1_15.

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Chen, Chunru, Tianhua Chen, Zhongmin Wang, Yanping Chen, and Hengshan Zhang. "An Outlier Detection Informed Aggregation Approach for Group Decision-Making." In Fuzzy Logic. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66474-9_7.

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Dubois, Didier, and Henri Prade. "Fuzzy Sets and Possibility Theory : Some Applications to Inference and Decision Processes." In Fuzzy Logic. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78023-3_4.

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Chen, Chunru, Tianhua Chen, Zhongmin Wang, Yanping Chen, and Hengshan Zhang. "Correction to: An Outlier Detection Informed Aggregation Approach for Group Decision-Making." In Fuzzy Logic. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66474-9_16.

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Carlsson, Christer, and Robert Fullér. "Fuzzy Sets and Fuzzy Logic." In Fuzzy Reasoning in Decision Making and Optimization. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1805-5_1.

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Carlsson, Christer, Mario Fedrizzi, and Robert Fullér. "Group Decision Support Systems." In Fuzzy Logic in Management. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-8977-2_3.

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Dubois, Didier, and Henri Prade. "Possibilistic Logic in Decision." In Fuzzy Logic and Soft Computing. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5261-1_1.

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Turunen, Esko. "Interpreting GUHA Data Mining Logic in Paraconsistent Fuzzy Logic Framework." In Algorithmic Decision Theory. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04428-1_25.

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Ben Amor, Nahla. "Possibilistic Graphical Models: From Reasoning to Decision Making." In Fuzzy Logic and Applications. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03200-9_10.

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Conference papers on the topic "Fuzzy logic decision"

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Aguzzoli, Stefano. "A linear space decision procedure for Gödel propositional logic." In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2016. http://dx.doi.org/10.1109/fuzz-ieee.2016.7737687.

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Wadgaonkar, Jagannath, and Kalyani Bhole. "Fuzzy logic based decision support system." In 2016 1st India International Conference on Information Processing (IICIP). IEEE, 2016. http://dx.doi.org/10.1109/iicip.2016.7975310.

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Goutam, Siddharth, Srija Unnikrishnan, Pradeep Singh, and Archana Karandikar. "Algorithm for handover decision using Fuzzy Logic." In 2020 IEEE Bombay Section Signature Conference (IBSSC). IEEE, 2020. http://dx.doi.org/10.1109/ibssc51096.2020.9332209.

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Oh, Jung-Min, Cheol-Soo Bang, and Geuk Lee. "Personal Color Decision System Using Fuzzy Logic." In 2008 International Conference on Convergence and Hybrid Information Technology. IEEE, 2008. http://dx.doi.org/10.1109/ichit.2008.282.

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Miron, Radu, and Tiberiu Letia. "Fuzzy logic decision in partial fingerprint recognition." In 2010 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR 2010). IEEE, 2010. http://dx.doi.org/10.1109/aqtr.2010.5520777.

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Kulkarni, Arun D., G. B. Giridhar, and Praveen Coca. "Neural-network-based fuzzy logic decision systems." In Photonics for Industrial Applications, edited by David P. Casasent. SPIE, 1994. http://dx.doi.org/10.1117/12.188914.

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Hudson, D., and M. Cohen. "Fuzzy logic in medical expert systems." In 26th IEEE Conference on Decision and Control. IEEE, 1987. http://dx.doi.org/10.1109/cdc.1987.272817.

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Yusuf, Hesham, and George Panoutsos. "Multi-criteria decision making using Fuzzy Logic and ATOVIC with application to manufacturing." In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2020. http://dx.doi.org/10.1109/fuzz48607.2020.9177772.

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Mitchell, Sophia, and Kelly Cohen. "Fuzzy logic decision making for autonomous robotic applications." In 2014 IEEE 6th International Conference on Awareness Science and Technology (iCAST). IEEE, 2014. http://dx.doi.org/10.1109/icawst.2014.6981843.

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Neffati, A., S. Caux, and M. Fadel. "Double fuzzy logic decision in HEV energy management." In 2013 World Electric Vehicle Symposium and Exhibition (EVS27). IEEE, 2013. http://dx.doi.org/10.1109/evs.2013.6915037.

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Reports on the topic "Fuzzy logic decision"

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Paule, Bernard, Flourentzos Flourentzou, Tristan de KERCHOVE d’EXAERDE, Julien BOUTILLIER, and Nicolo Ferrari. PRELUDE Roadmap for Building Renovation: set of rules for renovation actions to optimize building energy performance. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541614638.

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Abstract:
In the context of climate change and the environmental and energy constraints we face, it is essential to develop methods to encourage the implementation of efficient solutions for building renovation. One of the objectives of the European PRELUDE project [1] is to develop a "Building Renovation Roadmap"(BRR) aimed at facilitating decision-making to foster the most efficient refurbishment actions, the implementation of innovative solutions and the promotion of renewable energy sources in the renovation process of existing buildings. In this context, Estia is working on the development of inference rules that will make it possible. On the basis of a diagnosis such as the Energy Performance Certificate, it will help establishing a list of priority actions. The dynamics that drive this project permit to decrease the subjectivity of a human decisions making scheme. While simulation generates digital technical data, interpretation requires the translation of this data into natural language. The purpose is to automate the translation of the results to provide advice and facilitate decision-making. In medicine, the diagnostic phase is a process by which a disease is identified by its symptoms. Similarly, the idea of the process is to target the faulty elements potentially responsible for poor performance and to propose remedial solutions. The system is based on the development of fuzzy logic rules [2],[3]. This choice was made to be able to manipulate notions of membership with truth levels between 0 and 1, and to deliver messages in a linguistic form, understandable by non-specialist users. For example, if performance is low and parameter x is unfavourable, the algorithm can gives an incentive to improve the parameter such as: "you COULD, SHOULD or MUST change parameter x". Regarding energy performance analysis, the following domains are addressed: heating, domestic hot water, cooling, lighting. Regarding the parameters, the analysis covers the following topics: Characteristics of the building envelope. and of the technical installations (heat production-distribution, ventilation system, electric lighting, etc.). This paper describes the methodology used, lists the fields studied and outlines the expected outcomes of the project.
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