Thematische Bibliographien / Fuzzy logic

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Auswahl der wissenschaftlichen Literatur zum Thema „Fuzzy logic“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 31. Juli 2024

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Zeitschriftenartikel zum Thema "Fuzzy logic"

BOBILLO, FERNANDO, MIGUEL DELGADO, JUAN GÓMEZ-ROMERO und UMBERTO STRACCIA. „JOINING GÖDEL AND ZADEH FUZZY LOGICS IN FUZZY DESCRIPTION LOGICS“. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 20, Nr. 04 (August 2012): 475–508. http://dx.doi.org/10.1142/s0218488512500249.

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Ontologies have succeeded as a knowledge representation formalism in many domains of application. Nevertheless, they are not suitable to represent vague or imprecise information. To overcome this limitation, several extensions to classical ontologies based on fuzzy logic have been proposed. Even though different fuzzy logics lead to fuzzy ontologies with very different logical properties, the combined use of different fuzzy logics has received little attention to date. This paper proposes a fuzzy extension of the Description Logic [Formula: see text] — the logic behind the ontology language OWL 2 — that joins Gödel and Zadeh fuzzy logics. We analyze the properties of the new fuzzy Description Logic in order to provide guidelines to ontology developers to exploit the best features of each fuzzy logic. The proposal also considers degrees of truth belonging to a finite set of linguistic terms rather than numerical values, thus being closer to real experts' reasonings. We prove the decidability of the combined logic by presenting a reasoning preserving procedure to obtain a crisp representation for it. This result is generalized to offer a similar reduction that can be applied when any other finite t -norms, t -conorms, negations or implications are considered in the logic.

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Metcalfe, George, und Franco Montagna. „Substructural fuzzy logics“. Journal of Symbolic Logic 72, Nr. 3 (September 2007): 834–64. http://dx.doi.org/10.2178/jsl/1191333844.

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AbstractSubstructural fuzzy logics are substructural logics that are complete with respect to algebras whose lattice reduct is the real unit interval [0, 1]. In this paper, we introduce Uninorm logic UL as Multiplicative additive intuitionistic linear logic MAILL extended with the prelinearity axiom ((A → B) ∧ t) V ((B → A)∧ t). Axiomatic extensions of UL include known fuzzy logics such as Monoidal t-norm logic MIX and Gödel logic G, and new weakening-free logics. Algebraic semantics for these logics are provided by subvarieties of (representable) pointed bounded commutative residuated lattices. Gentzen systems admitting cut-elimination are given in the framework of hypersequents. Completeness with respect to algebras with lattice reduct [0, 1] is established for UL and several extensions using a two-part strategy. First, completeness is proved for the logic extended with Takeuti and Titani's density rule. A syntactic elimination of the rule is then given using a hypersequent calculus. As an algebraic corollary, it follows that certain varieties of residuated lattices are generated by their members with lattice reduct [0, 1].

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Gehrke, Mai, Carol Walker und Elbert Walker. „A Mathematical Setting for Fuzzy Logics“. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 05, Nr. 03 (Juni 1997): 223–38. http://dx.doi.org/10.1142/s021848859700021x.

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The setup of a mathematical propositional logic is given in algebraic terms, describing exactly when two choices of truth value algebras give the same logic. The propositional logic obtained when the algebra of truth values is the real numbers in the unit interval equipped with minimum, maximum and -x=1-x for conjunction, disjunction and negation, respectively, is the standard propositional fuzzy logic. This is shown to be the same as three-valued logic. The propositional logic obtained when the algebra of truth values is the set {(a, b)|a≤ b and a,b∈[0,1]} of subintervals of the unit interval with component-wise operations, is propositional interval-valued fuzzy logic. This is shown to be the same as the logic given by a certain four element lattice of truth values. Since both of these logics are equivalent to ones given by finite algebras, it follows that there are finite algorithms for determining when two statements are logically equivalent within either of these logics. On this topic, normal forms are discussed for both of these logics.

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MIYAKOSHI, Masaaki. „Fuzzy Logic“. Journal of Japan Society for Fuzzy Theory and Systems 4, Nr. 1 (1992): 90–97. http://dx.doi.org/10.3156/jfuzzy.4.1_90.

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Chast, Roz. „Fuzzy Logic“. Scientific American 289, Nr. 2 (August 2003): 96. http://dx.doi.org/10.1038/scientificamerican0803-96.

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Chast, Roz. „Fuzzy Logic“. Scientific American 288, Nr. 6 (Juni 2003): 92. http://dx.doi.org/10.1038/scientificamerican0603-92.

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Klir, G. J. „Fuzzy logic“. IEEE Potentials 14, Nr. 4 (1995): 10–15. http://dx.doi.org/10.1109/45.468220.

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Chast, Roz. „Fuzzy Logic“. Scientific American 288, Nr. 3 (März 2003): 112. http://dx.doi.org/10.1038/scientificamerican0303-112.

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Chast, Roz. „Fuzzy Logic“. Scientific American 287, Nr. 6 (Dezember 2002): 139. http://dx.doi.org/10.1038/scientificamerican1202-139.

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Chast, Roz. „Fuzzy Logic“. Scientific American 289, Nr. 6 (Dezember 2003): 128. http://dx.doi.org/10.1038/scientificamerican1203-128.

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Dissertationen zum Thema "Fuzzy logic"

Cerami, Marco. „Fuzzy Description Logics from a Mathematical Fuzzy Logic point of view“. Doctoral thesis, Universitat de Barcelona, 2012. http://hdl.handle.net/10803/113374.

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Description Logic is a formalism that is widely used in the framework of Knowledge Representation and Reasoning in Artificial Intelligence. They are based on Classical Logic in order to guarantee the correctness of the inferences on the required reasoning tasks. It is indeed a fragment of First Order Predicate Logic whose language is strictly related to the one of Modal Logic. Fuzzy Description Logic is the generalization of the classical Description Logic framework thought for reasoning with vague concepts that often arise in practical applications. Fuzzy Description Logic has been investigated since the last decade of the 20th century. During the first fifteen years of investigation their semantics has been based on Fuzzy Set Theory. A semantics based on Fuzzy Set Theory, however, has been shown to have some counter-intuitive behavior, due to the fact that the truth function for the implication used is not the residuum of the truth function for the conjunction. In the meanwhile, Fuzzy Logic has been given a formal framework based on Many-valued Logic. This framework, called Mathematical Fuzzy Logic, has been proposed has the kernel of a mathematically well founded Fuzzy Logic. In this dissertation we propose a Fuzzy Description Logic whose semantics is based on Mathematical Fuzzy Logic as its mathematically well settled kernel. To this end we provide a novel notation that is strictly related to the notation that is used in Mathematical Fuzzy Logic. After having settled the notation, we investigate the hierarchies of description languages over different-“t” norm based semantics and the reductions that can be performed between reasoning tasks. The new framework that we establish gives us the possibility to systematically investigate the relation of Fuzzy Description Logic to Fuzzy First Order Logic and Fuzzy Modal Logic. Next we provide some (un)decidability results for the case of infinite “t”-norm based semantics with or without knowledge bases. Finally we investigate the complexity bounds of reasoning tasks without knowledge bases for basic Fuzzy Description Logics over finite “t”-norms.
El trabajo desarrollado en esta tesis es una propuesta de sistematizar la formalización de las Lógicas de la Descripción Fuzzy a partir de la Lógica Difusa Matemática. Para ello se define un lenguaje para las Lógicas de la Descripción Fuzzy que extiende el lenguaje de la primera tradición de esta disciplina para adaptarlo al lenguaje más propio de la Lógica Difusa Matemática. Desde el punto de vista semántico, la teoría de conjuntos borrosos cede el paso a una semántica algebraica, que es la que se utiliza en la Lógica Difusa Matemática y que resuelve las consecuencias poco intuitivas que tenía la semántica tradicional. A partir de esta formalización, se tratan temas que eran tradicionales en las Lógicas de la Descripción clásicas como son las jerarquías de inclusiones entre lenguajes de la descripción y la relación de las Lógicas de la Descripción Fuzzy con la Lógica Difusa de primer orden por un lado y la Lógica Difusa Multi-modal por el otro. En relación a problemas de decidibilidad se demuestra que la satisfacción y la subsunción de conceptos en el lenguaje ALE bajo una semántica basada en la Lógica del Producto son problemas decidibles. También se demuestra que la consistencia de bases de conocimiento en el lenguaje ALC bajo una semántica basada en la Lógica de Lukasiewicz es un problema indecidible. En relación a problemas de complejidad computacional se demuestra que satisfacción y validez de fórmulas en la Lógica Modal minimal de Lukasiewicz con valores finitos son problemas PSPACE-completos. También se demuestra que la satisfacción y subsunción de conceptos en el lenguaje IALCED bajo una semántica basada en cualquier lógica difusa con valores finitos son problemas PSPACE-completos. Otra contribución de nuestro trabajo es el estudio sistemático de algoritmos de decisión para la satisfacción y subsunción de conceptos en el lenguaje IALCED, respecto a modelos “witnessed", basados en una reducción de es- tos problemas a los problemas de satisfacción y consecuencia en la lógica proposicional correspondiente.

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Chen, Guiming. „Fuzzy FOIL: A fuzzy logic based inductive logic programming system“. Thesis, University of Ottawa (Canada), 1996. http://hdl.handle.net/10393/9621.

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In many domains, characterizations of a given attribute are imprecise, uncertain and incomplete in the available learning examples. The definitions of classes may be vague. Learning systems are frequently forced to deal with such uncertainty. Traditional learning systems are designed to work in the domains where imprecision and uncertainty in the data are absent. Those learning systems are limited because of their impossibility to cope with uncertainty--a typical feature of real-world data. In this thesis, we developed a fuzzy learning system which combines inductive learning with a fuzzy approach to solve problems arising in learning tasks in the domains affected by uncertainty and vagueness. Based on Fuzzy Logic, rather than pure First Order Logic used in FOIL, this system extends FOIL with learning fuzzy logic relation from both imprecise examples and background knowledge represented by Fuzzy Prolog. The classification into the positive and negative examples is allowed to be a degree (of positiveness or negativeness) between 0 and 1. The values of a given attribute in examples need not to be the same type. Symbolic and continuous data can exist in the same attribute, allowing for fuzzy unification (inexact matching). An inductive learning problem is formulated as to find a fuzzy logic relation with a degree of truth, in which a fuzzy gain calculation method is used to guide heuristic search. The Fuzzy FOIL's ability of learning the required fuzzy logic relations and dealing with vague data enhances FOIL's usefulness.

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Coupland, Simon C. „Geometric fuzzy logic systems“. Thesis, De Montfort University, 2006. http://hdl.handle.net/2086/10782.

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There has recently been a significant increase in academic interest in the field oftype-2 fuzzy sets and systems. Type-2 fuzzy systems offer the ability to model and reason with uncertain concepts. When faced with uncertainties type-2 fuzzy systems should, theoretically, give an increase in performance over type-l fuzzy systems. However, the computational complexity of generalised type-2 fuzzy systems is significantly higher than type-l systems. A direct consequence of this is that, prior to this thesis, generalised type-2 fuzzy logic has not yet been applied in a time critical domain, such as control. Control applications are the main application area of type-l fuzzy systems with the literature reporting many successes in this area. Clearly the computational complexity oftype-2 fuzzy logic is holding the field back. This restriction on the development oftype-2 fuzzy systems is tackled in this research. This thesis presents the novel approach ofdefining fuzzy sets as geometric objects - geometric fuzzy sets. The logical operations for geometric fuzzy sets are defined as geometric manipulations of these sets. This novel geometric approach is applied to type-I, type-2 interval and generalised type-2 fuzzy sets and systems. The major contribution of this research is the reduction in the computational complexity oftype-2 fuzzy logic that results from the application of the geometric approach. This reduction in computational complexity is so substantial that generalised type-2 fuzzy logic has, for the first time, been successfully applied to a control problem - mobile robot navigation. A detailed comparison between the performance of the generalised type-2 fuzzy controller and the performance of the type-l and type-2 interval controllers is given. The results indicate that the generalised type-2 fuzzy logic controller outperforms the other robot controllers. This outcome suggests that generalised type-2 fuzzy systems can offer an improved performance over type-l and type-2 interval systems.

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Lundqvist, Patrik, und Michael Enhörning. „Speltestning : Med Fuzzy Logic“. Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-20260.

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Vid design av ett dataspel försöker speldesignern ofta skapa banor och fiender som tvingar spelaren att använda olika strategier för att överleva. För att hitta dessa strategier krävs speltestning. Speltestning är tidskrävande och då också dyrt. Den enklaste metoden för att spara tid är då att använda data hooks i spelet och sedan låta testpersoner spela spelet. Data samlas då in under alla spelsessioner och lagras i loggfiler.Med hjälp av data hooks samlades data in till denna rapport. Spelet som analyserades var ett spel av typen top-down-shooter. Anledning till att detta spel valdes var att spelet är ett exempel från Microsoft där designen är enligt XNAs standard, samt att spelidén är allmänt känd.Är det då möjligt att hitta tydliga strategier i den insamlade datan med hjälp av data mining och fuzzy logic? Det är definitivt möjligt att hitta tydliga strategier. Den insamlade datan från spelsessioner analyserades med hjälp av data mining, fuzzy logic och verktyget G-REX. Det visade sig att det fanns tydliga regler för att särskilja bra spelare från dåliga spelare. Detta visar att det är möjligt att utläsa spelarens strategi samt att jämföra denna mot hur speldesignern tänkt ut när han skapat spelet.Det som är mest intressant från resultatet är att G-REX hittade regler som sa hur en bra spelare skulle spela, och att vissa av dessa regler inte stämde överrens med hur speldesignern tänkt. En sådan regel var att i ett av passen var det bra att förlora hälsa, just därför att då kom det fler fiender och spelaren hann få mer poäng under passet.Vid jämförelse av de olika fuzzifieringstyperna (ramverket mot G-REX) visade sig att testresultatet blev väldigt likt varandra. Det innebär att allt arbete med ramverkets fuzzifiering inte hade behövts. Det innebär även att speldesigners med liten eller ingen kunskap alls om fuzzy logic skulle kunna använda G-REX till att evaluera sina spel och loggfiler med hjälp av data mining och fuzzy logic.

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Genito, Daniele. „Some topics in fuzzy logic“. Doctoral thesis, Universita degli studi di Salerno, 2010. http://hdl.handle.net/10556/113.

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2008-2009
Si trattano diversi aspetti della logica fuzzy, in particolare: 1) le proprietà preservate da un modello fuzzy ogniqualvolta esso è sottoposto a qualche genere di modifica; 2) la programmazione logica fuzzy, la logica della similarità e la metaprogrammazione, considerando la relazione di sinonimia tra predicati; 3) la connessione tra logica fuzzy e teoria dei bireticoli per il trattamento sia della verità che del grado di informazione.
VIII n.s.

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García, Z. Yohn E. „Fuzzy logic in process control : a new fuzzy logic controller and an improved fuzzy-internal model controller“. [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001552.

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GarcÃa, Z. Yohn E. „Fuzzy logic in process control: A new fuzzy logic controller and an improved fuzzy-internal model controller“. Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/2529.

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Two fuzzy controllers are presented. A fuzzy controller with intermediate variable designed for cascade control purposes is presented as the FCIV controller. An intermediate variable and a new set of fuzzy logic rules are added to a conventional Fuzzy Logic Controller (FLC) to build the Fuzzy Controller with Intermediate Variable (FCIV). The new controller was tested in the control of a nonlinear chemical process, and its performance was compared to several other controllers. The FCIV shows the best control performance regarding stability and robustness. The new controller also has an acceptable performance when noise is added to the sensor signal. An optimization program has been used to determine the optimum tuning parameters for all controllers to control a chemical process. This program allows obtaining the tuning parameters for a minimum IAE (Integral absolute of the error). The second controller presented uses fuzzy logic to improve the performance of the convention al internal model controller (IMC). This controller is called FAIMCr (Fuzzy Adaptive Internal Model Controller). Twofuzzy modules plus a filter tuning equation are added to the conventional IMC to achieve the objective. The first fuzzy module, the IMCFAM, determines the process parameters changes. The second fuzzy module, the IMCFF, provides stability to the control system, and a tuning equation is developed for the filter time constant based on the process parameters. The results show the FAIMCr providing a robust response and overcoming stability problems. Adding noise to the sensor signal does not affect the performance of the FAIMC.The contributions presented in this work include:The development of a fuzzy controller with intermediate variable for cascade control purposes. An adaptive model controller which uses fuzzy logic to predict the process parameters changes for the IMC controller. An IMC filter tuning equation to update the filter time constant based in the process paramete rs values. A variable fuzzy filter for the internal model controller (IMC) useful to provide stability to the control system.

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Al-Khalidi, Aous Nahad. „Evaluating Quality of Ziggurat of Ur Using Fuzzy Logic Concept and Fuzzy Logic Models“. The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1244041809.

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Aranibar, Luis Alfonso Quiroga. „Learning fuzzy logic from examples“. Ohio : Ohio University, 1994. http://www.ohiolink.edu/etd/view.cgi?ohiou1176495652.

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Hoyle, W. J. „Fuzzy logic, control and optimisation“. Thesis, University of Canterbury. Mechanical Engineering, 1996. http://hdl.handle.net/10092/6458.

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This thesis examines the utility of fuzzy logic in the field of control engineering. A tutorial introduction to the field of fuzzy control is presented during the development of an efficient fuzzy controller. Using the controller as a starting point, a set of criteria are developed that ensure a close connection between rule base construction and control surface geometry. The properties of the controller are exploited in the design of a global controller optimiser based on a genetic algorithm, and a tutorial explaining how the optimiser may be used to effect automatic controller design is given. A library of software that implements a fast fuzzy controller, a genetic algorithm, and various utility routines is included.

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Bücher zum Thema "Fuzzy logic"

Carter, Jenny, Francisco Chiclana, Arjab Singh Khuman und Tianhua Chen, Hrsg. Fuzzy Logic. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66474-9.

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Wang, Paul P., Da Ruan und Etienne E. Kerre, Hrsg. Fuzzy Logic. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71258-9.

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Lowen, R., und M. Roubens, Hrsg. Fuzzy Logic. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2014-2.

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Trillas, Enric, und Luka Eciolaza. Fuzzy Logic. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14203-6.

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Bothe, Hans-Heinrich. Fuzzy Logic. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-21929-4.

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Gerla, Giangiacomo. Fuzzy Logic. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9660-2.

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Bothe, Hans-Heinrich. Fuzzy Logic. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-07357-5.

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Reusch, Bernd, Hrsg. Fuzzy Logic. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78694-5.

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Patyra, M. J., und D. M. Mlynek, Hrsg. Fuzzy Logic. Wiesbaden: Vieweg+Teubner Verlag, 1996. http://dx.doi.org/10.1007/978-3-322-88955-3.

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Reusch, Bernd, Hrsg. Fuzzy Logic. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78023-3.

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Buchteile zum Thema "Fuzzy logic"

El Alaoui, Mohamed. „Fuzzy Logic“. In Fuzzy TOPSIS, 31–39. First edition. | Boca Raton : CRC Press, 2021.: CRC Press, 2021. http://dx.doi.org/10.1201/9781003168416-3-3.

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Gerla, Giangiacomo. „Truth-Functional Logic and Fuzzy Logic“. In Fuzzy Logic, 151–69. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9660-2_8.

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Bandemer, Hans. „Fuzzy Analysis of Fuzzy Data“. In Fuzzy Logic, 385–94. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2014-2_36.

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Buckley, James J., und Esfandiar Eslami. „Logic“. In An Introduction to Fuzzy Logic and Fuzzy Sets, 5–19. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1799-7_2.

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Trillas, Enric, und Luka Eciolaza. „Fuzzy Relations“. In Fuzzy Logic, 117–29. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14203-6_4.

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Trillas, Enric, und Luka Eciolaza. „Fuzzy Arithmetic“. In Fuzzy Logic, 141–58. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14203-6_6.

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Trillas, Enric, und Luka Eciolaza. „Fuzzy Measures“. In Fuzzy Logic, 159–74. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14203-6_7.

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Voigt, Michael, und Horst J. Bessai. „Fuzzy Equalization“. In Fuzzy Logic, 62–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78694-5_7.

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Bothe, Hans-Heinrich. „Fuzzy-Regelung“. In Fuzzy Logic, 136–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-21929-4_8.

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Bothe, Hans-Heinrich. „Fuzzy-Regelung“. In Fuzzy Logic, 140–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-07357-5_8.

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Konferenzberichte zum Thema "Fuzzy logic"

Guest, Clark C. „Optical fuzzy logic processor“. In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.fdd7.

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Fuzzy logic provides a means of working with qualitative data and rules. Like neural networks, fuzzy logic provides a mapping from a space of input variables to a space of output variables. Unlike neural networks, fuzzy logic provides a direct means of incorporating human expertise. For a given problem, the computational requirements for a fuzzy logic solution are often significantly less than those required by a neural network solution. However, fuzzy logic implementations are faced with a need for global communication in the distribution and collection of data. An optical processing architecture has been designed that allows for the global communication paths required by fuzzy logic. Multiple fuzzy rules can be processed in parallel. The fuzzy nature of the data is maintained throughout the system, thus preserving accuracy and fault tolerance. As a final step, a centroid is formed for each fuzzy output variable, providing non-fuzzy (crisp) values for additional processing.

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Zhao, Jidi, und Harold Boley. „Combining Fuzzy Description Logics and Fuzzy Logic Programs“. In 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. IEEE, 2008. http://dx.doi.org/10.1109/wiiat.2008.363.

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Cerami, Marco, Angel Garcia-Cerdana und Francesc Esteva. „From classical Description Logic to n-graded Fuzzy Description Logic“. In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584114.

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Das, Abhijit, Umapada Pal, Miguel Angel Ferrer Ballester und Michael Blumenstein. „Fuzzy logic based selera recognition“. In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891684.

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Burda, Michal. „Linguistic fuzzy logic in R“. In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2015. http://dx.doi.org/10.1109/fuzz-ieee.2015.7337826.

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Greenfield, Sarah, Francisco Chiclana und Scott Dick. „Interval-valued complex fuzzy logic“. In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2016. http://dx.doi.org/10.1109/fuzz-ieee.2016.7737939.

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Konstantopoulos, Stasinos, und Angelos Charalambidis. „Formulating description logic learning as an Inductive Logic Programming task“. In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584417.

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Gorman, Kevin J., und Kourosh J. Rahnamai. „Rapid Prototyping of Fuzzy Logic Controllers“. In ASME 1996 Design Engineering Technical Conferences and Computers in Engineering Conference. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/96-detc/cie-1450.

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Abstract The rapid prototyping of fuzzy logic controllers is accomplished by using the tools Matlab, Simulink, Fuzzy Logic Toolkit, and Real-Time Workshop. Device drivers were developed for Simulink for interfacing with DT2801 and DT2821 data acquisition boards. The fuzzy logic inference engine for the Fuzzy Logic Toolkit was modified to allow the systems to work as independent programs and to be downloadable to DSP (Digital Signal Processing) boards. Simulink is used to graphically implement fuzzy logic controllers. The Real-Time Workshop is used to compile blocks from Simulink into C code, then into an independent executable program, both on the PC and a dSpace DSP (Digital Signal Processing) board. Graphical interfaces are created and debugged by using dSPACE’s tools, Cockpit and Trace. By combining these tools, real-time fuzzy logic controllers are developed in laboratory environments.

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Esteva, Francese, Lluis Godo und Ricardo Oscar Rodriguez. „On the relation between modal and multi-modal logics over Łukasiewicz logic“. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015703.

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Srikanth, K., F. W. Liou und S. N. Balakrishnan. „Automatic Tolerance Assignment Using Fuzzy Logic“. In ASME 1998 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/detc98/dac-5585.

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Abstract Tolerance design is interdisciplinary in nature and requires input from various stages of product development such as design, manufacturing, and assembly. The factors involved, such as equipment and labor costs, process information, and required rate of successful assembly, are highly uncertain, especially in the early design stage. This paper presents a fuzzy logic approach to model all uncertain parameters in the process for automatic optimal tolerance assignment. A method based on fuzzy logic to evaluate assembly rate is proposed, and the comparison between the fuzzy logic approach and the statistical method is discussed. It is found that using fuzzy logic, a relatively smaller sample size is needed to estimate the assembly rate with reasonable accuracy. Two cases studies are used to demonstrate the process of integrating cost, manufacturing processes, and assembly rates and their uncertainties using fuzzy logic.

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Berichte der Organisationen zum Thema "Fuzzy logic"

Tailor, Sanjay. Fuzzy Logic. Fort Belvoir, VA: Defense Technical Information Center, Mai 1996. http://dx.doi.org/10.21236/ada310470.

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Meitzler, Thomas J., David Bednarz, E. J. Sohn, Kimberly Lane und Darryl Bryk. Fuzzy Logic Based Image Fusion. Fort Belvoir, VA: Defense Technical Information Center, Juli 2002. http://dx.doi.org/10.21236/ada405123.

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Combs, James E. Advanced Control Techniques with Fuzzy Logic. Fort Belvoir, VA: Defense Technical Information Center, Juni 2014. http://dx.doi.org/10.21236/ada604019.

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Bharadwaj, Arjun, und Jerry M. Mendel. Fuzzy Logic for Unattended Ground Sensor Fusion. Fort Belvoir, VA: Defense Technical Information Center, Januar 2006. http://dx.doi.org/10.21236/ada444339.

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Almufti, Ali. Parallel Hybrid Vehicles using Fuzzy Logic Control. Fort Belvoir, VA: Defense Technical Information Center, Dezember 2009. http://dx.doi.org/10.21236/ada513229.

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Borgwardt, Stefan, Felix Distel und Rafael Peñaloza. Gödel Description Logics: Decidability in the Absence of the Finitely-Valued Model Property. Technische Universität Dresden, 2013. http://dx.doi.org/10.25368/2022.199.

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In the last few years there has been a large effort for analysing the computational properties of reasoning in fuzzy Description Logics. This has led to a number of papers studying the complexity of these logics, depending on their chosen semantics. Surprisingly, despite being arguably the simplest form of fuzzy semantics, not much is known about the complexity of reasoning in fuzzy DLs w.r.t. witnessed models over the Gödel t-norm. We show that in the logic G-IALC, reasoning cannot be restricted to finitely valued models in general. Despite this negative result, we also show that all the standard reasoning problems can be solved in this logic in exponential time, matching the complexity of reasoning in classical ALC.

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Borgwardt, Stefan, Marco Cerami und Rafael Peñaloza. Subsumption in Finitely Valued Fuzzy EL. Technische Universität Dresden, 2015. http://dx.doi.org/10.25368/2022.212.

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Aus der Einleitung: Description Logics (DLs) are a family of knowledge representation formalisms that are successfully applied in many application domains. They provide the logical foundation for the Direct Semantics of the standard web ontology language OWL2. The light-weight DL EL, underlying the OWL2 EL profile, is of particular interest since all common reasoning problems are polynomial in this logic, and it is used in many prominent biomedical ontologies like SNOMEDCT and the Gene Ontology.

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Liang, Qilian. Energy Efficient Wireless Sensor Networks Using Fuzzy Logic. Fort Belvoir, VA: Defense Technical Information Center, Juni 2005. http://dx.doi.org/10.21236/ada434605.

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Liang, Qilian. Energy Efficient Wireless Sensor Networks Using Fuzzy Logic. Fort Belvoir, VA: Defense Technical Information Center, Dezember 2003. http://dx.doi.org/10.21236/ada419061.

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Wojcik, Frank A. Human Factors Reach Comfort Determination Using Fuzzy Logic. Fort Belvoir, VA: Defense Technical Information Center, Dezember 2009. http://dx.doi.org/10.21236/ada517381.

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