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1
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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2008-2009Si 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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King, Jonathan R. „New applications of fuzzy logic“. Thesis, University of East Anglia, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365021.
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Morillas, Gómez Samuel. „Fuzzy metrics and fuzzy logic for colour image filtering“. Doctoral thesis, Universitat Politècnica de València, 2008. http://hdl.handle.net/10251/1879.
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El filtrado de imagen es una tarea fundamental para la mayoría de los sistemas de visión por computador cuando las imágenes se usan para análisis automático o, incluso, para inspección humana. De hecho, la presencia de ruido en una imagen puede ser un grave impedimento para las sucesivas tareas de procesamiento de imagen como, por ejemplo, la detección de bordes o el reconocimiento de patrones u objetos y, por lo tanto, el ruido debe ser reducido.
En los últimos años el interés por utilizar imágenes en color se ha visto incrementado de forma significativa en una gran variedad de aplicaciones. Es por esto que el filtrado de imagen en color se ha convertido en un área de investigación interesante. Se ha observado ampliamente que las imágenes en color deben ser procesadas teniendo en cuenta la correlación existente entre los distintos canales de color de la imagen. En este sentido, la solución probablemente más conocida y estudiada es el enfoque vectorial. Las primeras soluciones de filtrado vectorial, como por ejemplo el filtro de mediana vectorial (VMF) o el filtro direccional vectorial (VDF), se basan en la teoría de la estadística robusta y, en consecuencia, son capaces de realizar un filtrado robusto. Desafortunadamente, estas técnicas no se adaptan a las características locales de la imagen, lo que implica que usualmente los bordes y detalles de las imágenes se emborronan y pierden calidad. A fin de solventar este problema, varios filtros vectoriales adaptativos se han propuesto recientemente.
En la presente Tesis doctoral se han llevado a cabo dos tareas principales: (i) el estudio de la aplicabilidad de métricas difusas en tareas de procesamiento de imagen y (ii) el diseño de nuevos filtros para imagen en color que sacan provecho de las propiedades de las métricas difusas y la lógica difusa. Los resultados experimentales presentados en esta Tesis muestran que las métricas difusas y la lógica difusa son herramientas útiles para diseñar técnicas de filtrado,Morillas Gómez, S. (2007). Fuzzy metrics and fuzzy logic for colour image filtering [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1879
Palancia
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Ali, Agha Rehmat. „Predicted Speed Control based on Fuzzy Logic for Belt Conveyors : Fuzzy Logic Control for Belt Conveyors“. Thesis, Karlstads universitet, Avdelningen för fysik och elektroteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-70106.
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In order to achieve energy savings for belt conveyor system, speed control provides one of the best solutions. Most of the traditional belt conveyors used in the industries are based on constant speed for all operational times. Due to the need and advancements in technology, Variable Frequency Drives (VFD) are employed in industries for a number of processes. Passive Speed Control was previously suggested for the proper utilization of VFD to make belt conveyor systems more power e- cient with increased life expectancy and reduced environmental eects including the noise reduction caused by constant speed of operation. Due to certain conditions and nature of operation of belt conveyor systems, it is not desirable to use Passive Speed control where feeding rate is random. Due to the extreme non-linearity of the random feeding rate, an Active speed control for VFD is desired which adjusts belt speed according to the material loading. In this thesis an Active Speed control for VFD is proposed that can achieve energy and cost ecient solutions for belt conveyor systems as well as avoiding half-lled belt operations. The aim of this thesis work is primarily to determine reliability and validity of Active Speed Control in terms of power savings. Besides achieving power savings, it is also necessary to check the economic feasibility. A detailed study is performed on the feasibility of Active Speed Control for random feeding rate according to industrial requirements. Due to the random and non-linearity of the material loading on the belt conveyor systems, a fuzzy logic algorithm is developed using the DIN 22101 model. The developed model achieves Active Speed Control based on the feeding rate and thereby optimizes the belt speed as required. This model also overcomes the risks of material spillage, overloading and sudden jerks caused due to unpredicted rise and fall during loading. The model conserves 20- 23% of the total power utilized compared to the conventional conveyor systems in use. However it is noticed that the peak power of conventional conveyor belt systems is up to 16% less compared to the proposed model. If implemented in dierent industries, based on the operational time and total consumption of electricity, the proposed Active speed control system is expected to achieve economic savings up to 10-12 % .
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Fung, Yun-hoi. „Linguistic fuzzy-logic control of autonomous vehicles /“. Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19660583.
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Chou, Te-Shun. „Fuzzy special logic functions and applications“. FIU Digital Commons, 1992. http://digitalcommons.fiu.edu/etd/2347.
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In this thesis, four special logic functions (threshold functions, monotone increasing functions, monotone decreasing functions, and unate functions) are extended to more general functions which allows the activities of these special functions to be a "fuzzy" rather than a "1-or-O" process. These special logic functions are called as fuzzy special logic functions and are based on the concepts and techniques developed in fuzzy logic and fuzzy languages. The algorithms of determining C(n), Cmax(n) and generating the most dissimilar fuzzy special logic functions as well as important properties and results are investigated. Examples are given to illustrated these special logic functions. In addition, their applications -- function representation, data compression, error correction, and monotone flash analog to digital converter, their relationships, and fuzzy classification are also presented. It is obviously shown that fuzzy logic theory can be used successfully on these four special logic functions in order to normalize the grade of membership function μ in the interval [0 1]. As a result, the techniques described in this thesis may be of use in the study of other special logic functions and much fertile field work is great worth researching and developing.
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Eatherley, Graham J. „Autonomous vehicle docking using fuzzy logic“. Thesis, University of Ottawa (Canada), 1994. http://hdl.handle.net/10393/6731.
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Autonomous control of robot vehicles has been studied recently by AI researchers with simulations and implementations emerging using both Neural Networks and Fuzzy Systems. The application is important primarily for autonomous control of planetary surface rovers and deep space vehicles where telepresence control (remote human operator) is precluded because of the long signal transit time. The problem of vehicle docking is specifically addressed in this thesis and the results of experiments on a small model truck and trailer using a mechanical based position sensing systems is reported. Fuzzy logic controls both forward and backward motion of the vehicle to successfully dock the trailer. Additionally, it is shown how the position sensing system can be improved using a small laser-based bar code engine. Future research including use of the system on other mobile robot platforms is discussed and we describe other research planned for the platform. In this thesis the author shows that the use of fuzzy logic for vehicle control simplifies the analysis of motion required to produce proper docking. In particular, the discontinuity of control produced by switching from forward to reverse is readily handled by fuzzy logic.
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Farah, Hassan. „The fuzzy logic control of aircraft“. Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0003/MQ43339.pdf.
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Marriott, Jack. „Adaptive robust fuzzy logic control design“. Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/15819.
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Wang, Jian Zhou. „Robust control with fuzzy logic algorithms“. Thesis, University of Edinburgh, 1997. http://hdl.handle.net/1842/13195.
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This thesis presents the results of an investigation of the robustness of the widely used Mandani-type fuzzy logic control systems under a wide variation of parameters of the controlled process. The measurements of the dynamic performance and system robustness of a control system were firstly defined from the engineering point of view, and the concepts of the robust space and the robustness index were introduced. The robustness of the FLC systems was investigated by analyzing the structure of the fuzzy rule base and membership functions of the input-output variables. Based on the close relation of the fuzzy rule base and the system dynamic trajectory on the phase plane, a switching line method is introduced to qualitatively analyze the dynamic performance of the SISO FLC systems. This switching line method enables the qualitative prediction of the shape and position of the robust space of the FLC controlled first order processes and second order processes. The effects of FLC parameters on system robustness were also investigated. The movements of the position and the shape of the switching line with the variation of the controller parameters were analyzed, and its relation with the system performance was reported. Three methods were proposed to improve the robustness of the FLC system. The first design method proposed was based on the switching line characteristics of the FLC system. The second method, called phase advanced FLC, was introduced to handle the control of high order processes with fuzzy algorithms. The third method was an evolutionary method based on the genetic algorithm which was used to automatically design a robust fuzzy control system, assuming the availability of the controlled process model.
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Dadone, Paolo. „Design Optimization of Fuzzy Logic Systems“. Diss., Virginia Tech, 2001. http://hdl.handle.net/10919/27893.
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Fuzzy logic systems are widely used for control, system identification, and pattern recognition problems. In order to maximize their performance, it is often necessary to undertake a design optimization process in which the adjustable parameters defining a particular fuzzy system are tuned to maximize a given performance criterion. Some data to approximate are commonly available and yield what is called the supervised learning problem. In this problem we typically wish to minimize the sum of the squares of errors in approximating the data.
We first introduce fuzzy logic systems and the supervised learning problem that, in effect, is a nonlinear optimization problem that at times can be non-differentiable. We review the existing approaches and discuss their weaknesses and the issues involved. We then focus on one of these problems, i.e., non-differentiability of the objective function, and show how current approaches that do not account for non-differentiability can diverge. Moreover, we also show that non-differentiability may also have an adverse practical impact on algorithmic performances.
We reformulate both the supervised learning problem and piecewise linear membership functions in order to obtain a polynomial or factorable optimization problem. We propose the application of a global nonconvex optimization approach, namely, a reformulation and linearization technique. The expanded problem dimensionality does not make this approach feasible at this time, even though this reformulation along with the proposed technique still bears a theoretical interest. Moreover, some future research directions are identified.
We propose a novel approach to step-size selection in batch training. This approach uses a limited memory quadratic fit on past convergence data. Thus, it is similar to response surface methodologies, but it differs from them in the type of data that are used to fit the model, that is, already available data from the history of the algorithm are used instead of data obtained according to an experimental design. The step-size along the update direction (e.g., negative gradient or deflected negative gradient) is chosen according to a criterion of minimum distance from the vertex of the quadratic model. This approach rescales the complexity in the step-size selection from the order of the (large) number of training data, as in the case of exact line searches, to the order of the number of parameters (generally lower than the number of training data). The quadratic fit approach and a reduced variant are tested on some function approximation examples yielding distributions of the final mean square errors that are improved (i.e., skewed toward lower errors) with respect to the ones in the commonly used pattern-by-pattern approach. Moreover, the quadratic fit is also competitive and sometimes better than the batch training with optimal step-sizes, thus showing an improved performance of this approach. The quadratic fit approach is also tested in conjunction with gradient deflection strategies and memoryless variable metric methods, showing errors smaller by 1 to 7 orders of magnitude. Moreover, the convergence speed by using either the negative gradient direction or a deflected direction is higher than that of the pattern-by-pattern approach, although the computational cost of the algorithm per iteration is moderately higher than the one of the pattern-by-pattern method. Finally, some directions for future research are identified.Ph. D.
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Cook, Brandon M. „Multi-Agent Control Using Fuzzy Logic“. University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1447688633.
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Farah, Hassan (Hassan Kahiye) Carleton University Dissertation Engineering Mechanical and Aerospace. „The Fuzzy logic control of aircraft“. Ottawa, 1999.
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馮潤開 und Yun-hoi Fung. „Linguistic fuzzy-logic control of autonomous vehicles“. Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B29812690.
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Patel, Chintan. „Evaluating Trench Safety Using Fuzzy Logic Concept and Fuzzy Set Models“. The Ohio State University, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=osu1419353000.
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Borgwardt, Stefan. „Fuzzy Description Logics with General Concept Inclusions“. Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-145311.
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Description logics (DLs) are used to represent knowledge of an application domain and provide standard reasoning services to infer consequences of this knowledge. However, classical DLs are not suited to represent vagueness in the description of the knowledge. We consider a combination of DLs and Fuzzy Logics to address this task. In particular, we consider the t-norm-based semantics for fuzzy DLs introduced by Hájek in 2005. Since then, many tableau algorithms have been developed for reasoning in fuzzy DLs. Another popular approach is to reduce fuzzy ontologies to classical ones and use existing highly optimized classical reasoners to deal with them. However, a systematic study of the computational complexity of the different reasoning problems is so far missing from the literature on fuzzy DLs. Recently, some of the developed tableau algorithms have been shown to be incorrect in the presence of general concept inclusion axioms (GCIs). In some fuzzy DLs, reasoning with GCIs has even turned out to be undecidable. This work provides a rigorous analysis of the boundary between decidable and undecidable reasoning problems in t-norm-based fuzzy DLs, in particular for GCIs. Existing undecidability proofs are extended to cover large classes of fuzzy DLs, and decidability is shown for most of the remaining logics considered here. Additionally, the computational complexity of reasoning in fuzzy DLs with semantics based on finite lattices is analyzed. For most decidability results, tight complexity bounds can be derived.
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Kutuva, Shanthanand R. „A Fuzzy Logic Based Virtual Surgery System“. University of Akron / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=akron1153967254.
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Al-Humaidi, Hanouf M. „A fuzzy logic approach to model delays in construction projects“. Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1185539202.
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Karakaya, Selami Tansel. „Coastal Scenic Assesment Using Fuzzy Logic Approach“. Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605661/index.pdf.
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This thesis analyzes the coastal landscape evaluation that is strongly rooted in the man-environment tradition. Scenery is a natural resource and managers need to attempt the evaluation of scenic resources in an objective and quantitative way that can be utilized mainly in landscape preservation and protection. The thesis will try to find an objective way in evaluation of the coastal scenery by using fuzzy logic mathematics and public perception studies. Using mathematical model developed within the frame work of the present study the selected 22 sites in Turkey were evaluated and then classified.
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Feng, Yi. „Dynamic Fuzzy Logic Control of GeneticAlgorithm Probabilities“. Thesis, Högskolan Dalarna, Datateknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3286.
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Genetic algorithms are commonly used to solve combinatorial optimizationproblems. The implementation evolves using genetic operators (crossover, mutation,selection, etc.). Anyway, genetic algorithms like some other methods have parameters(population size, probabilities of crossover and mutation) which need to be tune orchosen.In this paper, our project is based on an existing hybrid genetic algorithmworking on the multiprocessor scheduling problem. We propose a hybrid Fuzzy-Genetic Algorithm (FLGA) approach to solve the multiprocessor scheduling problem.The algorithm consists in adding a fuzzy logic controller to control and tunedynamically different parameters (probabilities of crossover and mutation), in anattempt to improve the algorithm performance. For this purpose, we will design afuzzy logic controller based on fuzzy rules to control the probabilities of crossoverand mutation. Compared with the Standard Genetic Algorithm (SGA), the resultsclearly demonstrate that the FLGA method performs significantly better.
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Wang, Xin. „A fuzzy logic based intelligent negotiation agent“. Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27194.
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With the evolution of electronic commerce (eCommerce) on the web and the rise of interests in intelligence of software agents, automated negotiation is becoming an increasingly popular method for an eCommerce system to be efficient; however, negotiation, which takes place in transaction, is complicated, time-consuming and costly for participants to reach an agreement. This thesis presents a model of an intelligent negotiation agent based on fuzzy logic methodology in order to alleviate the complexity of negotiation. The proposed negotiation agent model is particularly suitable to open environments, such as the Internet. The conventional methods, such as game theory, are incapable of handling an open environment where the information is sparse and full of uncertainty, while the fuzzy approaches are suitable to elegantly deal with this problem. The fuzzy logic based intelligent negotiation agent, presented in this thesis, is able to interact autonomously and consequently save human labor in negotiations. The aim of modeling a negotiation agent is to reach mutual agreement efficiently and intelligently. The negotiation agent is able to negotiate with other such agents, over various sets of issues, on behalf of the real-world parties they represent, i.e. it can handle multi-issue negotiation.
The reasoning model of the negotiation agent has been implemented partially by using c# based on Microsoft .NET. The reliability and the flexibility of the reasoning model are finally evaluated. The results show that performance of the proposed agent model is acceptable for negotiation parties to achieve mutual benefits.
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Saffiotti, Alessandro. „Autonomous robot navigation :a fuzzy logic approach“. Doctoral thesis, Universite Libre de Bruxelles, 1998. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/212077.
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Hariri, Ali. „An adaptive fuzzy logic power system stabilizer“. Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq20741.pdf.
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Menon, Vinay. „Fuzzy logic controller for an artificial heart“. Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq32405.pdf.
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Mian, Tariq M. „Fuzzy Logic based Automotive Airbag Control System“. Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0011/MQ52612.pdf.
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Ghodsi, Boushehri Ali. „Applying fuzzy logic to stock price prediction“. Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0015/MQ54332.pdf.
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Reynolds, Robert. „Gene Expression Data Analysis Using Fuzzy Logic“. Fogler Library, University of Maine, 2001. http://www.library.umaine.edu/theses/pdf/REynoldsR2001.pdf.
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Chiou, Fred Y. „User-interactive speech enhancement using fuzzy logic“. Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/14916.
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Bell, Michael Ray. „Fuzzy logic control of uncertain industrial processes“. Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/18998.
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Bennett, W. E. „Construction equipment emerging technologies: fuzzy logic controllers“. Thesis, Monterey, California. Naval Postgraduate School, 1995. http://hdl.handle.net/10945/25784.
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Dewa, Ismail bin. „VLSI implementation of fuzzy logic operator unit“. Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/28647.
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El-Deen, M. M. G. Naser. „Adaptive fuzzy logic control for solar buildings“. Thesis, Northumbria University, 2002. http://nrl.northumbria.ac.uk/2084/.
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Significant progress has been made on maximising passive solar heating loads through the careful selection of glazing, orientation and internal mass within building spaces. Control of space heating in buildings of this type has become a complex problem. Additionally, and in common with most building control applications, there is a need to develop control solutions that permit simple and transparent set up and commissioning procedures. This work concerns the development and testing of an adaptive control method for space heating in buildings with significant solar input. A simulation model of a building space to assess the performance of different control strategies is developed. A lumped parameter model based on an optimisation technique has been proposed and validated. It is shown that this model gives an improvement over existing low order modelling methods. A detailed model of a hot water heating system and related control devices is developed and evaluated for the specific purpose of control simulation. A PI-based fuzzy logic controller is developed in which the error and change of error between the internal air temperature and the user set point temperature is used as the controller input. A conventional PD controller is also considered for comparison. The parameters of the controllers are set to values that result in the best performance under likely disturbances and changes in setpoint. In a further development of the fuzzy logic controller, the Predicted Mean Vote (PMV) is used to control the indoor temperature of a space by setting it at a point where the PMV index becomes zero and the predicted percentage of persons dissatisfied (PPD) achieves a maximum threshold of 5%. The controller then adjusts the air temperature set point in order to satisfy the required comfort level given the prevailing values of other comfort variables contributing to the comfort sensation. The resulting controller is free of the set up and tuning problems that hinder conventional HVAC controllers. The need to develop an adaptive capability in the fuzzy logic controller to account for lagging influence of solar heat gain is established and a new adaptive controller has therefore been proposed. The development of a "quasi-adaptive" fuzzy logic controller is developed in two steps. A feedforward neural network is used to predict the internal air temperature, in which a singular value decomposition (SVD) algorithm is used to remove the highly correlated data from the inputs of the neural network to reduce the network structure. The fuzzy controller is then modified to have two inputs: the first input being the error between the setpoint temperature and the internal air temperature and the second the predicted future internal air temperature. When compared with a conventional method of control the proposed controller is shown to give good tracking of the setpoint temperature, reduced energy consumption and improved thermal comfort for the occupants by reducing solar overheating. The proposed controller is tested in real time using a test cell equipped with an oil- filled electric radiator, temperature and solar sensors. Experimental results confirm earlier findings arrived at by simulations, in that the proposed controller achieves superior tracking and reduces afternoon solar overheating, when compared with a conventional method of control.
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Lin, Yu-Jen. „Design of fuzzy logic controllers for FACTS“. Thesis, University of Strathclyde, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366675.
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BARBOSA, RAFAEL SILVERIO. „GAS TURBINE FAULT DIAGNOSIS USING FUZZY LOGIC“. PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2010. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=16198@1.
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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIROTurbinas a gás industriais modernas instaladas em usinas termelétricas têm seus parâmetros de desempenho monitorados em tempo real. Contudo, existem inúmeras falhas de operação que são impossíveis de serem detectadas pela simples visualização destes parâmetros, uma vez que a condição de operação do equipamento é influenciada por diversos fatores. Sistemas de diagnóstico são usualmente oferecidos pelos fabricantes destes equipamentos, mas não são divulgados na literatura aberta, que conta em geral com trabalhos aplicados a casos específicos e a turbinas aeronáuticas. Esta dissertação propõe um sistema de diagnóstico de falhas em turbinas a gás, o qual opera através da contínua comparação entre sinais medidos em campo, os quais são simulados por um programa computacional, e resultados gerados por um modelo de referência, simulador da turbina saudável. O sinal comparado serve de entrada para um sistema fuzzy, que identifica e quantifica a severidade das falhas. Foram testadas falhas fictícias no compressor e foi avaliada a influência da mudança de geometria na calibração do sistema. Os resultados mostraram a robustez do sistema e sua capacidade de aplicação em uma situação real.
Modern industrial gas turbines installed in thermal power plants have its performance parameters monitored in real time, however, there are innumerable operation faults that cannot be detected by a simple visual analysis of these parameters, once the equipment operating condition is influenced by several factors. Diagnosis systems are usually offered by the manufacturers of these equipments, but the methodologies are not published in the open literature, which is mostly dedicated to aircraft engines. This dissertation proposes a gas turbine diagnosis system that operates through the continuous comparison between the field measured signals, simulated by a software, and results generated by a reference numerical model, which represents the healthy gas turbine. The compared signal is used as input to a fuzzy system that identifies and quantifies the faults severity. Dummy compressor faults have been tested and the influence of the variable geometry has been analyzed during the system calibration. The results have shown the robustness of the system and its capability to be applied in a real world situation.
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Blewitt, William. „Exploration of emotion modelling through fuzzy logic“. Thesis, De Montfort University, 2012. http://hdl.handle.net/2086/6443.
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This work outlines a programme of research tasked with the exploration of representing psychologically grounded theories of emotion through fuzzy logic systems. It presents an introduction to the specific goals of the project, followed by an overview of the wider, multi-disciplinary field of emotion representation. Two emotion theories are explored in detail. One, rooted in behaviourism, proposed by J. R. Millenson in 1967; the other, the Geneva Emotion Wheel proposed by K. R. Scherer in 2005. Each of these theories is independently abstracted mathematically, and represented in terms of both type-1 and type-2 fuzzy logic systems. Six potential implementations of these systems are presented. Of these, five are tested within this report. The results of these tests are analysed and discussed in the context of both computational behaviour and psychological analogue. There follows a critical review where the effectiveness of the different implementations and models is considered, informed by both testing results and the psychology upon which they are based. A prototype of one implementation applied to govern the behaviour of an agent in a predator-prey scenario is included. Discussion of this prototype includes examples of how the implementation was practically applied to the environment, and an assessment of the behaviours of the agent in testing. The work concludes with an overview of the thesis, including discussion of the results of the project and future avenues of research related to the completed work. The contributions of the thesis are explicitly outlined: the research of pre-existing, psychologically grounded models of emotional state suitable for computational representation; construction of mathematical representations of two models of emotion, using both type-1 and type-2 fuzzy logic; and, the presentation of five computational implementations of those representations, of which four are explicitly tested, compared and critically reviewed.
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Mathur, Garima. „Fuzzy logic control for infant-incubator systems“. University of Akron / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=akron1153768682.
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Galor, Abraham. „Fuzzy logic control: The active cell method“. Case Western Reserve University School of Graduate Studies / OhioLINK, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=case1057689901.
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Edalath, Sanooj Sadique. „Fuzzy Logic Seismic Vibration Control of Buildings“. University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1335462916.
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Ebrahim, Rafee E. „Fuzzy logic programming based on alpha-cuts“. Thesis, De Montfort University, 2003. http://hdl.handle.net/2086/13274.
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Lin, Yuetong. „MODULAR CONSTRUCTION OF FUZZY LOGIC CONTROL SYSTEMS“. Diss., The University of Arizona, 2005. http://hdl.handle.net/10150/193845.
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This dissertation presents a novel approach to combining wavelet networks and multi-layer feedforward network for fuzzy logic control systems. Most of the existing methods focus on implementing the Takagi-Sugano fuzzy reasoning model and have demonstrated its effectiveness. However, these methods fail to keep the knowledge structure, which is critical in interpreting the learning process and providing insights to the working mechanism of the underlying systems. It is our intention here to continue the previous research by the PARCS group in this area by utilizing individual subnets to implement decision-making process with the fuzzy logic control systems based on the Mamdani model. Center Average defuzzification has seen its implementation by a neural network so that a succinct network structure is obtained. More importantly, wavelet networks have been adopted to provide better locality capturing capability and therefore better performance in terms of learning speed and training time. Offline orthogonal least squares method is used for training the wavelet subnets and the overall systems is updated using the steepest descent algorithm. Simulation results have shown the efficacy of this new approach in applications including system modeling and time series prediction.
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Targhetta, Walter <1994>. „A Trading system based on fuzzy logic“. Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/17959.
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