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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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Bell, K. R. W. "Artificial intelligence and uncertainty in power system operation." Thesis, University of Bath, 1995. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336238.
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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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Griffiths, Ian. "Microcontroller implementation of artificial intelligence for autonomous guided vehicles." Thesis, University of Wolverhampton, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266837.
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Allen, Michael James. "Artificial intelligence techniques for efficient object location in image sequences." Thesis, University of Wolverhampton, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343257.
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Wong, King-sau, and 黃敬修. "Improving the performance of lifts using artificial intelligence techniques." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B2768295X.
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(Uncorrected OCR)
Abstract of thesis entitled
Improving the Performance of Lifts Using Artificial Intelligence Techniques
submitted by
Wong King Sau
for the degree of Doctor of Philosophy
at the University of Hong Kong
in August 2003
An elevator group control system manages multiple elevators to serve hall
calls in a building. Most elevator group control systems need to recognize the traffic
pattern of the building and then change their control algorithms to improve the
efficiency of the elevator system. However, the traffic flow in a building is very
difficult to be classified into distinct patterns. Traffic recognition systems can
recognize certain traffic patterns, but mixed traffic patterns are difficult to be
recognized.
The aim of this study was therefore to develop improved duplex elevator
group control systems that do not need to recognize the traffic pattern. A fuzzy logic.
control unit and genetic algorithms control unit were used. A fuzzy logic control unit
integrates with the conventional duplex elevator group control system to improve
performance especially in mixed traffic patterns with intermittent heavy traffic
demand. This system will send more than one elevator to a floor with heavy demand, . according to the overall passenger traffic conditions in the building.
The genetic algorithms control unit divides the building into three zones and assigns an appropriate number of elevators to each zone. The floors covered by each zone are adjusted every five minutes. This control unit optimizes elevator group control by equalizing the number of hall calls in each zone, the total elevator door opening time in each zone, and the number of floors served by each elevator.
Both of the control units were tested by a simulator in a computer. The performance of the elevator system is given by indices such as average waiting time, wasted man-hour, and long waiting time percentage. The new performance index "wasted man-hour" indicates the total time spent by passengers in a building waiting for the lift service. Both proposed systems perform better than the conventional duplex control system.
(An abstract of 297 words.)
~
Signed _
Wong King Sauabstract
toc
Mechanical Engineering
Doctoral
Doctor of Philosophy
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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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Petermann, Bertrand. "Attitude control of small satellites using fuzzy logic." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ29622.pdf.
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Mullier, D. J. "The application of neural network and fuzzy logic techniques to educational hypermedia." Thesis, Leeds Beckett University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301039.
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Xu, Diancheng. "A fuzzy logic approach for chatter detection and suppression in end milling." Thesis, University of Ottawa (Canada), 2003. http://hdl.handle.net/10393/26351.
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In metal cutting processes, excessive vibration or chatter has an adverse effect on productivity and product surface quality. Various studies have been reported in the literature over the past few decades. However, the real application of the outcome of these studies has been very limited. A new system has been developed in this study for chatter detection and chatter suppression. The coherence function values of the frequency spectra from two accelerometers in orthogonal directions were used as a chatter indicator. The vibration energy was used to offset the over-vigilance behaviour of the coherence function. A fuzzy logic control approach was used for chatter suppression based on both the coherence function value and vibration energy level. To improve the adaptability of the fuzzy controller, a self-learning algorithm has also been developed for on-line updating the fuzzy rule base. A direct output tuning method was also proposed to improve the responsiveness of the system.
The proposed system has been tested using both steel and aluminium workpieces with and without thin-walls. The experimental results show that the proposed system worked reasonably well for on-line chatter detection and suppression.
The thesis also explored the possibility of using the coherence function for chatter prediction. The verification of its feasibility may be carried out in the future.
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Joorabian, M. "Application of artificial intelligence for accurate fault location on transmission systems." Thesis, University of Bath, 1996. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336234.
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Chrysanthakopoulos, Georgios. "A fuzzy-logic autonomous agent, applied as a supervisory controller in a simulated environment /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/6044.
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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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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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Rustum, Rabee. "Modelling activated sludge wastewater treatment plants using artificial intelligence techniques (fuzzy logic and neural networks)." Thesis, Heriot-Watt University, 2009. http://hdl.handle.net/10399/2207.
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Activated sludge process (ASP) is the most commonly used biological wastewater treatment system. Mathematical modelling of this process is important for improving its treatment efficiency and thus the quality of the effluent released into the receiving water body. This is because the models can help the operator to predict the performance of the plant in order to take cost-effective and timely remedial actions that would ensure consistent treatment efficiency and meeting discharge consents. However, due to the highly complex and non-linear characteristics of this biological system, traditional mathematical modelling of this treatment process has remained a challenge. This thesis presents the applications of Artificial Intelligence (AI) techniques for modelling the ASP. These include the Kohonen Self Organising Map (KSOM), backpropagation artificial neural networks (BPANN), and adaptive network based fuzzy inference system (ANFIS). A comparison between these techniques has been made and the possibility of the hybrids between them was also investigated and tested. The study demonstrated that AI techniques offer viable, flexible and effective modelling methodology alternative for the activated sludge system. The KSOM was found to be an attractive tool for data preparation because it can easily accommodate missing data and outliers and because of its power in extracting salient features from raw data. As a consequence of the latter, the KSOM offers an excellent tool for the visualisation of high dimensional data. In addition, the KSOM was used to develop a software sensor to predict biological oxygen demand. This soft-sensor represents a significant advance in real-time BOD operational control by offering a very fast estimation of this important wastewater parameter when compared to the traditional 5-days bio-essay BOD test procedure. Furthermore, hybrids of KSOM-ANN and KSOM-ANFIS were shown to result much more improved model performance than using the respective modelling paradigms on their own.
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Nainar, Irshad. "An adaptive fuzzy logic controller for intelligent networking and control." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 1996. https://ro.ecu.edu.au/theses/1466.
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In this thesis, we present a fuzzy logic control scheme to regulate the flow of traffic approaching a set of intersections. An adaptive Fuzzy Logic Traffic Controller (FLTC) is used to adjust the green phase split of the north-south and east-west approaches of a set of traffic signals based on the actual traffic approaching the intersection. Each intersection is coordinated with its neighbouring intersections by adjusting the offset of the local intersection. The offset is adjusted by a local fuzzy logic controller loacted at each intersection. A new fuzzy control scheme, using a supervisory Fuzzy Logic Controller, is also proposed for adjusting the offset. The fuzzy knowledge base of the supervisory Fuzzy Logic Controller is automatically generated by Genetic Algorithms (GAs). The fuzzy rules generated by the integrated Fuzzy Logic and Genetic Algorithm architecture is found to be effective in optimising the traffic flow. The effectiveness of the above fuzzy control scheme is established through simulations of the traffic flow approaching an isolated intersection, two adjacent intersections, and a set of three intersections. The superiority of adjusting offset using a supervisory fuzzy logic controller is established through simulations.
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Reutzel, Edward W. "On the limitations and extensions of bidirectional associative memories in neural networks and fuzzy logic control theory." Thesis, Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/16870.
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Leite, Manuela Souza. "Projeto, montagem e instrumentação de um protótipo experimental de sistema de polimerização para o desenvolvimento e implementação de diferentes técnicas de controles inteligentes." [s.n.], 2011. http://repositorio.unicamp.br/jspui/handle/REPOSIP/266835.
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Orientador: Flavio Vasconcelos da SilvaTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química
Made available in DSpace on 2018-08-19T07:29:45Z (GMT). No. of bitstreams: 1 Leite_ManuelaSouza_D.pdf: 7752104 bytes, checksum: ab0250de44ea5b7e99ae3740fec4f538 (MD5) Previous issue date: 2011
Resumo: Através de processos de polimerização pode-se obter uma enorme diversidade de produtos de grande interesse industrial, devido a sua vasta gama de aplicações. Cada reação deve ser feita com finalidades específicas, e então os polímeros devem ser produzidos sob certas condições operacionais estabelecidas, que lhe atribuem características relacionadas às suas aplicações. Buscando soluções para os diversos desafios existentes em processos de polimerização, tem sido crescente o número de trabalhos que buscam novas estratégias de controle mais eficazes para tais sistemas. O controle de um reator de polimerização apresenta grande dificuldade devido a sua natureza altamente não-linear e complexidade do mecanismo cinético da reação. Muitos dos algoritmos convencionais de controle não atendem, em sua totalidade, as exigências cada vez mais especificas destes processos e, visando atender estas necessidades, tem ocorrido nos últimos anos, um crescimento considerável na aplicação de estratégias de controle avançado em processos de polimerização. Este trabalho teve como proposta a montagem de um protótipo experimental, utilização de tecnologia de automação, desenvolvimento e implementação de estratégias de controle baseadas em inteligência artificial, especificamente, lógica fuzzy. A configuração do sistema experimental permitiu o acompanhamento em tempo real das principais variáveis do processo, possibilitando o uso de medidas on-line de variáveis, como viscosidade e densidade, as quais estão relacionadas indiretamente com o peso molecular e conversão, respectivamente. As estratégias de controle foram desenvolvidas com o objetivo de proporcionar a manutenção da temperatura da reação de polimerização em solução, e em batelada, do estireno, uma vez que esta variável possui influência direta na qualidade do produto final. O estireno foi selecionado como estudo de caso devido a sua importância industrial e ampla faixa de aplicação. Foram implementados no sistema experimental controladores mono e multivariáveis, e com saídas do tipo incremental e posicional, utilizando-se de modelos Mamdani e Sugeno. A reação foi conduzida por 3 horas, a um set-point de 90°C, utilizando uma concentração do monômero es tireno de 50% em volume, e como solvente o tolueno, tendo o BPO (peróxido de benzoíla) como iniciador. Foi definido um sistema de controle tipo cascata-fuzzy, o qual trata-se de um sistema que apresenta inediticidade, visto que não foram encontradas na literatura aplicações com esta configuração. Os controladores inteligentes foram aplicados na malha principal, tendo como variável de saída (variável intermediária) a temperatura da camisa do reator, e a variação de potência da resistência imersa no fluido térmico da camisa, como variável manipulada deste processo. As estratégias de controle, implementadas para um mesmo estudo de caso, foram analisadas conforme suas características, e todas as estruturas apresentaram eficiência do controle da reação de polimerização. A eficiência do sistema foi avaliada através do comportamento das variáveis controlada e manipulada, análise de índices de desempenho dos controladores (ISE, ITSE, IAE e ITAE), consumo de energia elétrica, visando redução de custos operacionais e, análise das propriedades finais do polímero obtido tais como: peso molecular médio, polidispersidade e produtividade (conversão)
Abstract: The favorable properties of polymeric products such as its usage, flexibility, light weight, low cost and its ease of processing, results in increased on their demand. Polymerization reactors have nonlinear natures and they show time varying behaviour. Their dynamic nature and the wide variations in operating conditions during batch cycles can make the reactor control difficult and important. Temperature variations greatly affect the kinetics of polymerization process and the produced polymer. As a result, to keep the product quality constant, the temperature of the reactor should be efficiently controlled. However, the control of polymerization reactors in general and particularly batch polymerization reactors is very difficult due to its complex characteristics. Advanced control techniques can be used as a viable solution for controlling and improving the efficiency and productivity of such nonlinear processes. Until recently, application of intelligent system such as fuzzy logic control in batch polymerization reactor control has been realized. This work included the installation of an experimental prototype, automation techniques, development and implementation of fuzzy control strategies in a batch polymerization reactor. The experimental system allowed realtime monitoring of key process variables. The design enables on-line measurement of variables indirectly related to the molecular weight and conversion, such as viscosity and density. A free radical polymerization of styrene was chosen as a process for the investigation, because polystyrene is an important product in today's industrial polymers and it has a very wide range of applications. In this process, temperature control is the most important control problem. Styrene (50%, v/v), toluene and benzoylperoxide (BPO) were used as the monomer, solvent and initiator, respectively. The reaction was conducted for 3 hours at a set point of 90 °C. Setting an unprecede nted fuzzy-cascade was employed. The intelligent controllers have been applied in the primary loop. The secondary variable was the temperature of the reactor jacket, and the power variation of resistance immersed in a heat transfer fluid that circulates through the jacket, as manipulated variable in this process. The experimental results show the effectiveness of fuzzy controller strategies. System efficiency was evaluated through the behavior of the controlled and manipulated variables, analysis of performance indices of the controllers (ISE, ITSE, IAE and ITAE), energy consumption, to reduce costs operational and analysis of the final properties of the polymer obtained such as average molecular weight, polydispersity and conversion
Doutorado
Sistema de Processos Quimicos e Informatica
Doutor em Engenharia Química
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Nezbedová, Katarína. "Aplikace fuzzy logiky pro hodnocení kvality zákazníků." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2020. http://www.nusl.cz/ntk/nusl-417708.
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This master thesis deals with the evaluation of SAP customer quality using fuzzy logic theory and its application. The core of the work is to create models in the MATLAB and Microsoft Excel development environment. Also to use it to create and compare ratings of several customers with different parameters. Using the results, the company can determine the following procedure to solve customer’s problems.
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Nekulová, Iveta. "Riziko výběru dodavatele s využitím fuzzy logiky." Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2017. http://www.nusl.cz/ntk/nusl-367530.
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This diploma thesis deals with the evaluation of security companies for ZETOR TRACTORS a.s. using fuzzy logic models. The main part of the thesis consists of proposals for the evaluation of the suppliers' evaluation of the company. Decision models are created in Microsoft Excel and Matlab. Another part of the thesis deals with analysis and comparison of results from both programs.
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Vala, Lukáš. "Riziko výběru dodavatele s využitím fuzzy logiky." Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2017. http://www.nusl.cz/ntk/nusl-367531.
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The diploma thesis deals with evaluation of fertilizer suppliers using fuzzy logic models. The main part of the thesis consists of proposals for the evaluation of the company's suppliers. Decision models are created in Microsoft Excel and Matlab. Another part of the thesis deals with analysis and comparison of results from both programs.
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Yellanki, Sampath Kumar. "Kidney Compatibility Score Generation for a Donor - Recipient pair using Fuzzy Logic." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1345153510.
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Keneni, Blen M. Keneni. "Evolving Rule Based Explainable Artificial Intelligence for Decision Support System of Unmanned Aerial Vehicles." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1525094091882295.
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Matthews, Stephen. "Learning lost temporal fuzzy association rules." Thesis, De Montfort University, 2012. http://hdl.handle.net/2086/8257.
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Fuzzy association rule mining discovers patterns in transactions, such as shopping baskets in a supermarket, or Web page accesses by a visitor to a Web site. Temporal patterns can be present in fuzzy association rules because the underlying process generating the data can be dynamic. However, existing solutions may not discover all interesting patterns because of a previously unrecognised problem that is revealed in this thesis. The contextual meaning of fuzzy association rules changes because of the dynamic feature of data. The static fuzzy representation and traditional search method are inadequate. The Genetic Iterative Temporal Fuzzy Association Rule Mining (GITFARM) framework solves the problem by utilising flexible fuzzy representations from a fuzzy rule-based system (FRBS). The combination of temporal, fuzzy and itemset space was simultaneously searched with a genetic algorithm (GA) to overcome the problem. The framework transforms the dataset to a graph for efficiently searching the dataset. A choice of model in fuzzy representation provides a trade-off in usage between an approximate and descriptive model. A method for verifying the solution to the hypothesised problem was presented. The proposed GA-based solution was compared with a traditional approach that uses an exhaustive search method. It was shown how the GA-based solution discovered rules that the traditional approach did not. This shows that simultaneously searching for rules and membership functions with a GA is a suitable solution for mining temporal fuzzy association rules. So, in practice, more knowledge can be discovered for making well-informed decisions that would otherwise be lost with a traditional approach.
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Podder, Tanmay. "ANALYSIS & STUDY OF AI TECHNIQUES FORAUTOMATIC CONDITION MONITORING OFRAILWAY TRACK INFRASTRUCTURE : Artificial Intelligence Techniques." Thesis, Högskolan Dalarna, Datateknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4757.
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Since the last decade the problem of surface inspection has been receiving great attention from the scientific community, the quality control and the maintenance of products are key points in several industrial applications.The railway associations spent much money to check the railway infrastructure. The railway infrastructure is a particular field in which the periodical surface inspection can help the operator to prevent critical situations. The maintenance and monitoring of this infrastructure is an important aspect for railway association.That is why the surface inspection of railway also makes importance to the railroad authority to investigate track components, identify problems and finding out the way that how to solve these problems. In railway industry, usually the problems find in railway sleepers, overhead, fastener, rail head, switching and crossing and in ballast section as well. In this thesis work, I have reviewed some research papers based on AI techniques together with NDT techniques which are able to collect data from the test object without making any damage. The research works which I have reviewed and demonstrated that by adopting the AI based system, it is almost possible to solve all the problems and this system is very much reliable and efficient for diagnose problems of this transportation domain. I have reviewed solutions provided by different companies based on AI techniques, their products and reviewed some white papers provided by some of those companies. AI based techniques likemachine vision, stereo vision, laser based techniques and neural network are used in most cases to solve the problems which are performed by the railway engineers.The problems in railway handled by the AI based techniques performed by NDT approach which is a very broad, interdisciplinary field that plays a critical role in assuring that structural components and systems perform their function in a reliable and cost effective fashion. The NDT approach ensures the uniformity, quality and serviceability of materials without causing any damage of that materials is being tested. This testing methods use some way to test product like, Visual and Optical testing, Radiography, Magnetic particle testing, Ultrasonic testing, Penetrate testing, electro mechanic testing and acoustic emission testing etc. The inspection procedure has done periodically because of better maintenance. This inspection procedure done by the railway engineers manually with the aid of AI based techniques.The main idea of thesis work is to demonstrate how the problems can be reduced of thistransportation area based on the works done by different researchers and companies. And I have also provided some ideas and comments according to those works and trying to provide some proposal to use better inspection method where it is needed.The scope of this thesis work is automatic interpretation of data from NDT, with the goal of detecting flaws accurately and efficiently. AI techniques such as neural networks, machine vision, knowledge-based systems and fuzzy logic were applied to a wide spectrum of problems in this area. Another scope is to provide an insight into possible research methods concerning railway sleeper, fastener, ballast and overhead inspection by automatic interpretation of data.In this thesis work, I have discussed about problems which are arise in railway sleepers,fastener, and overhead and ballasted track. For this reason I have reviewed some research papers related with these areas and demonstrated how their systems works and the results of those systems. After all the demonstrations were taking place of the advantages of using AI techniques in contrast with those manual systems exist previously.This work aims to summarize the findings of a large number of research papers deploying artificial intelligence (AI) techniques for the automatic interpretation of data from nondestructive testing (NDT). Problems in rail transport domain are mainly discussed in this work. The overall work of this paper goes to the inspection of railway sleepers, fastener, ballast and overhead.
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Peterek, Daniel. "Vyhodnocení dodavatelského rizika prostřednictvím fuzzy logiky." Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-414170.
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The presented diploma thesis deals with the evaluation of suppliers for the company Ferrit using fuzzy logic. The main part of the diploma thesis deals with the creation of proposals for the solution of the evaluation of suppliers of a selected company. Decision models are created in Microsoft Excel and MATLAB. The comparison of the results of both proposed models is the content of the part of the work.
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Chlebana, Martin. "Aplikace fuzzy logiky při výběrů dodavatele." Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2013. http://www.nusl.cz/ntk/nusl-232755.
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Master's thesis deals with the selection of suitable suppliers and evaluating their possible risks for building business with the help of using artificial intelligence methods. In thesis are processed theoretical foundations, description and analysis of the problem and suggested own solutions.
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Abdullah, Rudwan Ali Abolgasim. "Intelligent methods for complex systems control engineering." Thesis, University of Stirling, 2007. http://hdl.handle.net/1893/257.
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This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning). Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions.
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Sanchez, Edinzo J. Iglesias. "Using fuzzy logic to enhance control performance of sliding mode control and dynamic matrix control." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001497.
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Aslan, Muhittin. "Modeling The Water Quality Of Lake Eymir Using Artificial Neural Networks (ann) And Adaptive Neuro Fuzzy Inference System (anfis)." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12610211/index.pdf.
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Lakes present in arid regions of Central Anatolia need further attention with regard to water quality. In most cases, mathematical modeling is a helpful tool that might be used to predict the DO concentration of a lake. Deterministic models are frequently used to describe the system behavior. However most ecological systems are so complex and unstable. In case, the deterministic models have high chance of failure due to absence of priori information. For such cases black box models might be essential. In this study DO in Eymir Lake located in Ankara was modeled by using both Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Phosphate, Orthophospate, pH, Chlorophyll-a, Temperature, Alkalinity, Nitrate, Total Kjeldahl Nitrogen, Wind, Precipitation, Air Temperature were the input parameters of ANN and ANFIS. The aims of these modeling studies were: to develop models with ANN to predict DO concentration in Lake Eymir with high fidelity to actual DO data, to compare the success (prediction capacity) of ANN and ANFIS on DO modeling, to determine the degree of dependence of different parameters on DO. For modeling studies &ldquoMatlab R 2007b&rdquo
software was used. The results indicated that ANN has high prediction capacity of DO and ANFIS has low with respect to ANN. Failure of ANFIS was due to low functionality of Matlab ANFIS Graphical User Interface. For ANN Modeling effect of meteorological data on DO data on surface of the lake was successfully described and summer month super saturation DO concentrations were successfully predicted.
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Konecny, Jan. "Isotone fuzzy Galois connections and their applications in formal concept analysis." Diss., Online access via UMI:, 2009.
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Thesis (Ph. D.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Systems Science and Industrial Engineering, 2009.Includes bibliographical references.
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Junior, Manoel Gadêlha de Freitas. "Sistema computacional de auxílio ao diagnóstico em síndromes coronarianas agudas." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/98/98131/tde-31102011-120827/.
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As síndromes coronarianas agudas são responsáveis por uma elevada taxa de mortalidade no Brasil e no Mundo. As falhas diagnósticas, principalmente quando o paciente é atendido em serviços de pronto socorro, por clínicos gerais, certamente contribuem para esse quadro, embora amenizadas pelos sistemas cardiológicos de tele-medicina. Entretanto, muitos serviços de emergência não têm acesso a esses sistemas e, além disso, possuem uma limitada capacidade diagnóstica em casos de coronariopatia aguda. Neste trabalho foi desenvolvido um sistema de inteligência artificial baseado na lógica \"fuzzy\", capaz de auxiliar um médico generalista no diagnóstico desses casos, sem fazer uso de tele-medicina, nem de exames laboratoriais. O sistema utiliza um eletrocardiógrafo interpretativo para suprir as deficiências do médico na análise do eletrocardiograma. Usando a história clínica, o exame físico e o laudo eletrocardiográfico automático, dados são inseridos em uma planilha Excel que fornece uma sugestão de diagnóstico e de respectiva conduta terapêutica. O sistema demonstrou um bom desempenho, sendo, assim, uma solução viável e de baixo custo para o diagnóstico precoce de síndromes coronarianas agudas em unidades primárias de pronto socorro.Acute coronary syndromes are responsible for a high mortality rate in Brazil and worldwide. Diagnostic failures, especially when the patient is treated in emergency services by general practitioners, certainly contribute to this condition, although tele-medicine cardiology systems are possibly responsible for the reduction of that mortality rate. However, many services do not have access to these systems and also have a limited diagnostic capacity for diagnosing cases of acute coronary disease. We have developed an artificial intelligence system using elements of \"fuzzy\" logic, capable of assisting a general practitioner in the diagnostic of these cases, without making use of tele-medicine or laboratory tests. The system uses an interpretive electrocardiograph that can overcome the general practitioners\' deficiencies in the analysis of the electrocardiogram. The physician, starting from the important elements of the clinical history, the physical examination and the electrocardiogram automatic report, enters data into an Excel program that will provide a suggestion of diagnostic and therapeutic management. The system is low cost and has shown great performance, so it is a viable solution to the problem of early diagnostic of acute coronary syndromes in primary emergency units.
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Mlčoch, Luboš. "Aplikace fuzzy logiky pro hodnocení rizikovosti firemních klientů." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2012. http://www.nusl.cz/ntk/nusl-223644.
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Diplomová práce se soustředí na aplikaci principů fuzzy logiky v procesu hodnocení rizikovosti firemních klientů. Na základě reálných dat poskytnutých bankou autor navrhnul dva různé modely, které slouží jako nástroje pro detekci úpadkových firemních klientů. Oba modely a jejich výkonnost jsou řádně otestovány.
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Brož, Zdeněk. "Fuzzy hodnocení investic - brownfield redevelopment." Doctoral thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2013. http://www.nusl.cz/ntk/nusl-233755.
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Tato disertační práce se zaměřuje na problematiku investování a podporu rozhodování pomocí moderních metod. Zejména pokud jde o analýzu, hodnocení a výběr tzv. brownfieldů pro jejich redevelopment (revitalizaci). Cílem této práce je navrhnout univerzální metodu, která usnadní rozhodovací proces. Proces rozhodování je v praxi komplikován též velkým počet relevantních parametrů ovlivňujících konečné rozhodnutí. Navržená metoda je založena na využití fuzzy logiky, modelování, statistické analýzy, shlukové analýzy, teorie grafů a na sofistikovaných metodách sběru a zpracování informací. Nová metoda umožňuje zefektivnit proces analýzy a porovnávání alternativních investic a přesněji zpracovat velký objem informací. Ve výsledku tak bude zmenšen počet prvků množiny nejvhodnějších alternativních investic na základě hierarchie parametrů stanovených investorem.
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Val, Petran. "BINOCULAR DEPTH PERCEPTION, PROBABILITY, FUZZY LOGIC, AND CONTINUOUS QUANTIFICATION OF UNIQUENESS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1504749439893027.
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PORTO, JUNIOR ALMIR C. S. "Desenvolvimento de um sistema de monitoração e diagnóstico utilizando lógica fuzzy aplicado às válvulas de controle de processo do CEA - Centro Experimental de ARAMAR." reponame:Repositório Institucional do IPEN, 2014. http://repositorio.ipen.br:8080/xmlui/handle/123456789/23596.
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Submitted by Claudinei Pracidelli (cpracide@ipen.br) on 2015-03-17T10:49:39Z
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Dissertação (Mestrado em Tecnologia Nuclear)
IPEN/D
Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP
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Moghaddaszadeh, Kermani Mohammad. "Criticality strategic decision making model for maintenance and asset management." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/criticality-based-strategic-decision-making-model-for-maintenance-and-asset-management(913ab341-1c44-480c-875e-77d8e28f037b).html.
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Over the last century, there has been growing interest in changing the approach to maintenance management. The current practice for selecting critical equipment and making a decision on the most appropriate maintenance strategy is perceived to have serious limitations, principally because it lacks decision analysis. Due to the complex nature of decision-making in maintenance management, different models have been developed for selecting critical equipment. However, many of these models considered maintenance management as operational concern and ignored the strategic concerns of maintenance management. This thesis builds upon earlier works on decision-making for selecting critical equipment and maintenance strategy. It sets out to construct three hypotheses by introducing evidence from a comprehensive literature review, case study analysis and in-depth interviews. The thesis focuses on artificial intelligence and multi-criteria decision-making techniques (i.e. Fuzzy Logic and Analytical Hierarchy Process) to bridge this gap. It proposes a strategic decision-making model in maintenance and asset management for selecting critical equipment and deciding on a maintenance strategy. The novelty of model is to propose an approach in which maintenance strategy can be applied based on the equipment criticality while not making a trade-off between safety and cost but rather to combine the concern of safety with financial, operational and technical perspectives. The model provides an opportunity to consider safety as the first priority. The research output suggests that existing criticality assessment methods for optimising maintenance delivery have limited value and are suffering from a lack of strategic decision analysis. Multi-criteria decision-making tools could be used to improve decision-making of criticality assessment methods and hence maintenance strategy implementation. The validity of the proposed strategic decision-making model was tested through case study analysis and in-depth interviews. The results suggest that a strategic decision-making model could have a significant impact on improving safety, reliability and operational availability. The strategic decision-making model would enable asset managers to track the consequences of their decisions whilst dealing with maintenance. It is also an effective tool in the hands of a maintenance department to convince their asset managers to make a maintenance investment.
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Korčáková, Michaela. "Riziko výběru dodavatele s využitím fuzzy logiky." Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2018. http://www.nusl.cz/ntk/nusl-382713.
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The diploma thesis deals with the draft of fuzzy model used for decisions of choosing the suppliers of the tool steel for the company S.CH.W.SERVICE, s.r.o. In the introduction of the thesis the theoretical basis for the process are summarized and the company is introduced. The main part consists of the actual suggestions for the evalutaion of the company´s suppliers. The deciosion making models are created in MS Excel and MATLAB. The last part of the thesis is dedicated to the comparison of the results from both suggested models.
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Friedl, Pavel. "Využití umělé inteligence pro snižování rizika v podniku." Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-446766.
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Presented diploma thesis is focused on evaluation of the supplier’s risk and the selection of the most suitable supplier with the use of artificial intelligence. The main part of the diploma thesis deals with the creation of the decision models. The decision models will be created in MS Excel and MATLAB based on the rules of the fuzzy logic. These models will determine the most suitable supplier for the company expert Elektro GOLA s.r.o. and they will also evaluate the supplier’s risk.
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Pospíšil, Radek. "Využití expertních systémů v marketingovém průzkumu." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2009. http://www.nusl.cz/ntk/nusl-222208.
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The master’s thesis “Utilization of expert systems in marketing research” deals with the development of an application based on an expert system, knowledge basis of which contains expert information from the area of CCTV security systems design. In this thesis, the used expert system is described and also its connection to the relevant marketing research is mentioned.The solution of the expert system is carried out in C++ language programming and the purpose of the program is to eliminate misunderstandings and inaccuracies which occur during the initial negotiations between the salesmen and potential investors.The created software product, based on the knowledge basis of the experts in the field and past experience with camera systems, helps the customers decide what type of system, in what range and with what configuration is suitable for them. Hereby the orientation customers is encouraged in the way as described by the modern concept of marketing.
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Hanlon, Nicholas P. "Neuro-Fuzzy Dynamic Programming for Decision-Making and Resource Allocation during Wildland Fires." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1321370261.
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Abicalil, Felipe Sertã. "Controle com lógica Fuzzy e Neurofuzzy aplicada à análise e programação de robôs móveis com visualização e simulação 3D." Universidade do Estado do Rio de Janeiro, 2007. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=766.
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Este trabalho tem como objetivo o estudo de uma área da robótica chamada robótica móvel. Um robô móvel deve realizar uma navegação segura e esta é a principal motivação deste trabalho. Para tal foi desenvolvido um simulador de robótica móvel com visualização em 3D. Um dos grandes interesses na área de robótica móvel é a utilização de algoritmos de inteligência artificial. O objetivo deste trabalho é a utilização e
simulação de inteligência artificial para o controle destinado ao desvio de obstáculos. As simulações são dinâmicas, ou seja, o robô não tem informação previa do cenário. Os algoritmos de inteligência artificial implementadas neste trabalho são lógica Fuzzy e Neurofuzzy. As contribuições do simulador são: a simulação e visualização em 3D com o cenário modelado em um programa CAD/3D, permite testar diversas configurações antes
de testar o robô real, simula o ruído de sensores, utiliza lógica fuzzy e neurofuzzy para o desvio de obstáculos. Os resultados mostram a capacidade do sistema fuzzy para lidar com os dados ruidosos dos sensores assim como a influência das variáveis antecedentes e conseqüentes do sistema fuzzy de no comportamento do robô móvel para o desvio de obstáculos além da capacidade do sistema neurofuzzy de aprender a partir dos dados de treinamento mostrando uma melhoria no resultado das simulações.This work has as objective the study of an area of the robotics named mobile robotics. A mobile robot must navigate in a safe way and this is the main motivation of this work. To do that a mobile robotics simulator with 3D visualization was developed. One of the great interests in mobile robotics is using artificial intelligence algorithms. The main point of this work is using and simulate artificial intelligence applied in obstacle avoidance control. The simulations are dynamics it means that the robot do not have previous information about the scenery. The artificial intelligence algorithms developed in this work are Fuzzy and Neurofuzzy logics. The simulator contributions are that the simulation and 3D visualization where the scenery is a 3D model from a CAD/3D software besides allows to test many configurations before testing the real robot and simulates noise from sensors and uses fuzzy and neurofuzzy logics to obstacle avoidance. The results show the fuzzy system capability to deal with the noisy data from sensors and how fuzzy variables influences the mobile robot behavior in obstacle avoidance besides the ability of neurofuzzy system to learn from training data showing improvements in the simulation results.
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Tseng, Chun-Hao. "Safety performance analyzer for constructed environments (SPACE)." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1148572816.
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Pierrard, Régis. "Explainable Classification and Annotation through Relation Learning and Reasoning." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPAST008.
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Avec les succés récents de l’apprentissage profond et les interactions toujours plus nombreuses entre êtres humains et intelligences artificielles, l’explicabilité est devenue une préoccupation majeure. En effet, il est difficile de comprendre le comportement des réseaux de neurones profonds, ce qui les rend inadaptés à une utilisation dans les systèmes critiques. Dans cette thèse, nous proposons une approche visant à classifier ou annoter des signaux tout en expliquant les résultats obtenus. Elle est basée sur l’utilisation d’un modèle transparent, dont le raisonnement est clair, et de relations floues interprétables qui permettent de représenter l’imprécision du langage naturel.Au lieu d’apprendre sur des exemples sur lesquels les relations ont été annotées, nous proposons de définir un ensemble de relations au préalable. L’évaluation de ces relations sur les exemples de la base d’entrainement est accélérée grâce à deux heuristiques que nous présentons. Ensuite, les relations les plus pertinentes sont extraites en utilisant un nouvel algorithme de frequent itemset mining flou. Ces relations permettent de construire des règles pour la classification ou des contraintes pour l’annotation. Ainsi, une explication en langage naturel peut être générée.Nous présentons des expériences sur des images et des séries temporelles afin de montrer la généricité de notre approche. En particulier, son application à l’annotation d’organe explicable a été bien évaluée par un ensemble de participants qui ont jugé les explications convaincantes et cohérentesWith the recent successes of deep learning and the growing interactions between humans and AIs, explainability issues have risen. Indeed, it is difficult to understand the behaviour of deep neural networks and thus such opaque models are not suited for high-stake applications. In this thesis, we propose an approach for performing classification or annotation and providing explanations. It is based on a transparent model, whose reasoning is clear, and on interpretable fuzzy relations that enable to express the vagueness of natural language.Instead of learning on training instances that are annotated with relations, we propose to rely on a set of relations that was set beforehand. We present two heuristics that make the process of evaluating relations faster. Then, the most relevant relations can be extracted using a new fuzzy frequent itemset mining algorithm. These relations enable to build rules, for classification, and constraints, for annotation. Since the strengths of our approach are the transparency of the model and the interpretability of the relations, an explanation in natural language can be generated.We present experiments on images and time series that show the genericity of the approach. In particular, the application to explainable organ annotation was received positively by a set of participants that judges the explanations consistent and convincing
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Costa, Bueno Vicente. "Fuzzy Horn clauses in artificial intelligence: a study of free models, and applications in art painting style categorization." Doctoral thesis, Universitat Autònoma de Barcelona, 2021. http://hdl.handle.net/10803/673374.
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Aquesta tesi doctoral contribueix a l’estudi de les clàusules de Horn en lògiques difuses, així com al seu ús en representació difusa del coneixement aplicada al disseny d’un algorisme de classificació de pintures segons el seu estil artístic. En la primera part del treball ens centrem en algunes nocions rellevants per a la programació lògica, com ho són per exemple els models lliures i les estructures de Herbrand en lògica matemàtica difusa. Així doncs, provem l’existència de models lliures en classes universals difuses de Horn, i demostrem que tota teoria difusa universal de Horn sense igualtat té un model de Herbrand. A més, introduïm dues nocions de minimalitat per a models lliures, i demostrem que aquestes nocions són equivalents en el cas de les fully named structures. En la segona part de la tesi doctoral, utilitzem les clàusules de Horn combinades amb el modelatge qualitatiu com a marc de representació difusa del coneixement per a la categorització d’estils de pintura artística. Finalment, dissenyem un classificador de pintures basat en clàusules de Horn avaluades, descriptors qualitatius de colors i explicacions. Aquest algorisme, anomenat l-SHE, proporciona raons dels resultats obtinguts i mostra percentatges competitius de precisió a l’experimentació.La presente tesis doctoral contribuye al estudio de las cláusulas de Horn en lógicas difusas, así como a su uso en representación difusa del conocimiento aplicada al diseño de un algoritmo de clasificación de pinturas según su estilo artístico. En la primera parte del trabajo nos centramos en algunas nociones relevantes para la programación lógica, como lo son por ejemplo los modelos libres y las estructuras de Herbrand en lógica matemática difusa. Así pues, probamos la existencia de modelos libres en clases universales difusas de Horn y demostramos que toda teoría difusa universal de Horn sin igualdad tiene un modelo de Herbrand. Asimismo, introducimos dos nociones de minimalidad para modelos libres, y demostramos que estas nociones son equivalentes en el caso de las fully named structures. En la segunda parte de la tesis doctoral, utilizamos cláusulas de Horn combinadas con el modelado cualitativo como marco de representación difusa del conocimiento para la categorización de estilos de pintura artística. Finalmente, diseñamos un clasificador de pinturas basado en cláusulas de Horn evaluadas, descriptores cualitativos de colores y explicaciones. Este algoritmo, que llamamos l-SHE, proporciona razones de los resultados obtenidos y obtiene porcentajes competitivos de precisión en la experimentación.
This PhD thesis contributes to the systematic study of Horn clauses of predicate fuzzy logics and their use in knowledge representation for the design of an art painting style classification algorithm. We first focus the study on relevant notions in logic programming, such as free models and Herbrand structures in mathematical fuzzy logic. We show the existence of free models in fuzzy universal Horn classes, and we prove that every equality-free consistent universal Horn fuzzy theory has a Herbrand model. Two notions of minimality of free models are introduced, and we show that these notions are equivalent in the case of fully named structures. Then, we use Horn clauses combined with qualitative modeling as a fuzzy knowledge representation framework for art painting style categorization. Finally, we design a style painting classifier based on evaluated Horn clauses, qualitative color descriptors, and explanations. This algorithm, called l-SHE, provides reasons for the obtained results and obtains percentages of accuracy in the experimentation that are competitive.
Universitat Autònoma de Barcelona. Programa de Doctorat en Ciència Cognitiva i Llenguatge
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Malinowski, Erich Lacerda. "Um aplicativo para a execução de sistemas especialistas no planejamento e controle da manutenção." Universidade Tecnológica Federal do Paraná, 2012. http://repositorio.utfpr.edu.br/jspui/handle/1/1462.
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Este trabalho apresenta o desenvolvimento de um aplicativo para a execução de Sistemas Especialistas baseados em Lógica Fuzzy, a fim de auxiliar gestores no diagnóstico do funcionamento de equipamentos e na determinação do momento ótimo da intervenção ou manutenção dos mesmos. O estudo teve como motivação a necessidade de superar algumas características do Sistema Especialista DEMOI, desenvolvido e executado na ferramenta Logic Fuzzy Toolbox do ambiente Matlab™. Assim, uma ferramenta em linguagem JAVA e C foi elaborada visando a obtenção de uma melhora no tempo de processamento das informações e a integração direta entre Sistemas Especialistas de monitoramento e de planejamento próprias das operações de manutenção. No desenvolvimento deste trabalho foram consideradas as etapas correspondentes aos testes preliminares de operação do sistema especialista já existente, bem como a elaboração do aplicativo proposto. Os resultados das simulações, realizadas sobre um caso prático de análise de vibração de uma máquina rotativa, mostraram os benefícios do novo aplicativo, principalmente na redução considerável no tempo de retorno para diagnósticos envolvendo elevadas quantidades de variáveis e regras de inferências. Adicionalmente, com a introdução da ferramenta, foi propiciado um ambiente de integração entre Sistemas Especialistas de monitoramento e planejamento da manutenção, que poderá ser utilizado por gestores sem as limitações existentes em aplicativos ou recursos de softwares comerciais.In this study, a development of an application for the execution of Expert Systems based on the Fuzzy Logic is carried out in order to help managers on the diagnosis of the operation of equipments and on the determination of the best moment for doing intervention or maintenance. The research had as motivation the necessity to overcome some characteristics of the Expert System DEMOI, developed and executed in the tool Fuzzy Logic Toolbox of the environment Matlab™. So, a tool in language JAVA and C was developed aiming to get an improvement in the processing time of the informations and the integration between Expert Systems of monitoring and planning of maintenance operations. For developing this research were considered preliminary tests of operation of the Expert Systems linked to Matlab software, as well as the development steps of the proposed application. The results of the simulations, carried out on a practical case of analysis of vibration of a rotary machine, showed the benefits of the new application, principally when the processing time was evaluated. In this sense, a considerable reduction in time of return for diagnoses with elevated quantities of variables and inferences rules was possible by using the new tool. Additionally, with the introduction of the application was provided an environment of integration between Expert Systems of monitoring and planning of the maintenance, which can be used for managers without the limitations present in applications of commercial softwares.
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Kobelka, Jiří. "Návrh automatického hodnocení rizika úvěru bankovních klientů." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2011. http://www.nusl.cz/ntk/nusl-222896.
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Diplomová práce se zabývá aplikací fuzzy logiky na proces automatické detekce úpadkového klienta z pohledu řízení úvěrového rizika banky. Na základě analýzy stávajícího informačního systému Credit Risk Monitoring autor navrhuje změnu přístupu v hodnocení úvěrového klienta.
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Richtrová, Kateřina. "Hodnocení klienta banky." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2015. http://www.nusl.cz/ntk/nusl-225063.
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The master’s thesis deals with the topic of the use of artificial intelligence for managerial decision making in the firm. This thesis contains proposal of model of fuzzy logic in MS Excel and MATLAB for evaluation of the client’s solvency of bank for the purposes of loan providing.
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Chrun, Ivan Rossato. "Mapas cognitivos fuzzy dinâmicos aplicados em vida artificial e robótica de enxame." Universidade Tecnológica Federal do Paraná, 2016. http://repositorio.utfpr.edu.br/jspui/handle/1/2512.
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ANPEste trabalho propõe o uso de Mapas Cognitivos Fuzzy Dinâmicos (DFCM, do inglês Dynamic Fuzzy Cognitive Maps), uma evolução dos Mapas Cognitivos Fuzzy (FCM), para o desenvolvimento de sistemas autônomos para tomada de decisões. O FCM representa o conhecimento de forma simbólica, através de conceitos e relações causais dispostas em um grafo. Na sua versão clássica, os FCMs são usados no desenvolvimento de modelos estáticos, sendo inapropriados para o desenvolvimento de modelos temporais ou dinâmicos devido à ocorrência simultânea de todas as causalidades em uma estrutura fixa dos grafos, i.e., os conceitos e suas relações causais são invariantes no tempo. O DFCM utiliza o mesmo formalismo matemático do FCM através de grafos, acrescentando funcionalidades, como por exemplo, a capacidade de auto adaptação através de algoritmos de aprendizagem de máquina e a possibilidade de inclusão de novos tipos de conceitos e relações causais ao modelo FCM clássico. A partir dessas inclusões, é possível construir modelos DFCM para tomada de decisões dinâmicas, as quais são necessárias no desenvolvimento de ferramentas inteligentes em áreas de conhecimento correlatas à engenharia, de modo especifico a construção de modelos aplicados em Robótica Autônoma. Em especial, para as áreas de Robótica de Enxame e Vida artificial, como abordados nesta pesquisa. O sistema autônomo desenvolvido neste trabalho aborda problemas com diferentes objetivos (como desviar de obstáculos, coletar alvos ou alimentos, explorar o ambiente), hierarquizando as ações necessárias para atingi-los, através do uso de uma arquitetura para o planejamento, inspirada no modelo clássico de Subsunção de Brooks, e uma máquina de estados para o gerenciamento das ações. Conceitos de aprendizagem de máquina, em especial Aprendizagem por Reforço, são empregadas no DFCM para a adaptação dinâmica das relações de casualidade, possibilitando o controlador a lidar com eventos não modelados a priori. A validação do controlador DFCM proposto é realizada por meio de experimentos simulados através de aplicações nas áreas supracitadas.
This dissertation proposes the use of Dynamic Fuzzy Cognitive Maps (DFCM), an evolution of Fuzzy Cognitive Maps (FCM), for the development of autonomous system to decision-taking. The FCM represents knowledge in a symbolic way, through concepts and causal relationships disposed in a graph. In its standard form, the FCMs are limited to the development of static models, in other words, classical FCMs are inappropriate for development of temporal or dynamic models due to the simultaneous occurrence of all causalities in a permanent structure, i.e., the concepts and the causal relationships are time-invariant. The DFCM uses the same mathematical formalism of the FCM, adding features to its predecessor, such as self-adaptation by means of machine learning algorithms and the possibility of inclusion of new types of concepts and causal relationships into the classical FCM model. From these inclusions, it is possible to develop DFCM models for dynamic decision-making problems, which are needed to the development of intelligent tools in engineering and other correlated areas, specifically, the construction of autonomous systems applied in Autonomous Robotic. In particular, to the areas of Swarm Robotics and Artificial Life, as approached in this research. The developed autonomous system deals with multi-objective problems (such as deviate from obstacle, collect target or feed, explore the environment), hierarchizing the actions needed to reach them, through the use of an architecture for planning, inspired by the Brook’s classical Subsumption model, and a state machine for the management of the actions. Learning machine algorithms, in particular Reinforcement Learning, are implemented in the DFCM to dynamically tune the causalities, enabling the controller to handle not modelled event a priori. The proposed DFCM model is validated by means of simulated experiments applied in the aforementioned areas.
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Tan, Arie Hadipriono. "The Integration of Fuzzy Fault Trees and Artificial Neural Networks to Enhance Satellite Imagery for Detection and Assessment of Harmful Algal Blooms." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574773012023708.
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