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1
Mac, Connell Peter Frederick Andrew. "Heating control using a knowledge-based approach." Thesis, University of Exeter, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296234.
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Dan, Qing. "A fuzzy rule-based approach for edge feature classification." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ39646.pdf.
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Osut, Demet. "A Behavior Based Robot Control System Using Neuro-fuzzy Approach." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/109765/index.pdf.
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In autonomous navigation of mobile robots the dynamic environment is a source of problems. Because it is not possible to model all the possible conditions, the key
point in the robot control is to design a system that is adaptable to different conditions and robust in dynamic environments.
This study presents a reactive control system for a Khepera robot with the ability to navigate in a dynamic environment for reaching goal objects. The main motivation of
this research is to design a robot control, which is robust to sensor errors and sudden changes and adaptable to different environments and conditions. Behavior based
approach is used with taking the advantage of fuzzy reasoning in design.
Experiments are made on Webots, which is a simulation environment for Khepera robot.
4
OLIVEIRA, CARLOS ALEXANDRE DOS SANTOS. "STRATEGIC GROUPS: ARESOURCE-BASED VIEW AND NEURO-FUZZY SYSTEMS APPROACH." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2004. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=5856@1.
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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRODesde sua formulação, no início da década de setenta, o conceito de grupo estratégico é objeto de pesquisas teóricas e empíricas que buscam confirmar sua existência, sua contribuição à avaliação da performance e à formação das estratégias das empresas. Este trabalho soma-se a estas pesquisas, utilizando os conceitos da Visão Resource- Based e a aplicação de ferramentas de inteligência computacional, neste caso as redes neurais e os sistemas de inferência fuzzy, com o objetivo de contribuir para a discussão deste tema na superação de suas limitações e dos novos desafios que o aumento da complexidade das arenas competitivas trouxeram para as pesquisas do gerenciamento estratégico. A Visão Resource-Based fornece a base teórica para o desenvolvimento dos construtos: grau de inimitabilidade e grau de imobilidade, resultantes da exploração estratégica dos recursos da empresa. Estes construtos são propostos como dimensões de avaliação da semelhança estratégica entre as empresas de uma arena competitiva. A inteligência computacional fornece os meios de extração de informações subjetivas, e presentes em ambientes complexos, através da simulação do aprendizado, percepção, evolução e adaptação do raciocínio humano. O resultado é a proposição de um modelo de avaliação da existência de grupos estratégicos, utilizando os construtos Grau de Inimitabilidade e Grau de Imobilidade, e Sistemas Neuro-fuzzy. Este modelo é aplicado ao setor de supermercados como teste de validação do mesmo.
Since its has introduced, in the beginning of the decade of seventy, the concept of strategic groups is object of theoretical and empirical research that aims to confirm its existence, its contribution to performance evaluation and the formulation of the strategies of the firms. This text join these research, using the Resource-Based Views framework and soft computing, in this case neural networks and fuzzy inference systems, with aims at contributing for the discussion of this subject to overcome its limitations and the new challenges, resulting increasingly complexity and competitive environment, for the strategic management research. The Resource-Based View framework supplies the theoretical underpinnings to use the inimitability degree and immobility degree, resultants of the strategical exploration of the resources of the firms, as constructors to evaluate firm strategic similarity in a competitive environment. Soft computing is a tool to extract subjective data from complexity environments, simulating the ability for learning, perception, evolution and adaptation of human reasoning. The result of this research is the proposal of a model to identify strategic groups, applying the constructors Inimitability Degree and Immobility Degree, and Neuro-fuzzy Inference Systems. To validate the model, a test is performed to the supermarkets industry.
5
Ernest, Nicholas D. "UAV Swarm Cooperative Control Based on a Genetic-Fuzzy Approach." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1337954828.
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Porro, Martorell Olga. "A hesitant fuzzy perceptual-based approach to model linguistic assessments." Doctoral thesis, Universitat Politècnica de Catalunya, 2021. http://hdl.handle.net/10803/672127.
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Multiple-criteria or multiple-attribute group decision-making is a sub-field of operations research that seek to find a common and representative solution given the preferences elicited by a pre-defined group, over a set of alternatives and with respect to a set of coherent criteria (or attributes). Recently, the modelling of natural language in these processes has captured the attention of many researchers. Most of the evaluations in a group-decision making context are inherently imprecise, incomplete or vague, and therefore, experts feel more comfortable using their language rather than numerical values.
The use of hesitant fuzzy linguistic term sets is one of the recent tools that enables the modelling of linguistic assessments in multiple-criteria decision-making. Nonetheless, advances in hesitant linguistic multi-attribute group decision making require the development of structures flexible enough to deal with unbalanced and multi-granular linguistic information. More tools are needed in order to really grasp the differences in the qualitative reasoning processes of each individual.
This thesis, firstly, introduces a perceptual-based distance able to capture differences between unbalanced linguistic assessments, which is based on a lattice structure of hesitant fuzzy linguistic terms. Secondly, this distance is used to define a perceptual-based centroid or central opinion which, in turn, is used to define a consensus measure or degree of agreement within the group. Thirdly, with the aim to deal with multi-perceptual group decision-making contexts, where each decision maker has its own qualitative reasoning approach, a perceptual-based transformation function and a projected algebraic structure are defined. The developed tools can deal with different multi-granularity linguistic environments.
Two applications are presented to demonstrate the utility, relevancy and feasibility of the methods. On the one hand, a specific perceptual-based classification and ranking method is introduced and applied to a real group decision making problem in an educational setting. This framework is used to classify and rank a set of secondary students according to their degree of entrepreneurial competency, which is based on real data provided by the Andorra Government. On the other hand, an extended fuzzy multi-perceptual linguistic TOPSIS is designed and applied to a real group decision making problem in the context of smart city governance. This perceptual extension is used to assess the criteria governing the strategic decision making process of energy multinational companies when deciding where to expand its sustainable services and products.El multiple-criteria o bé multi-attributte group decision-making (MCGDM / MAGDM) és una branca del camp de OR (operations research) l'objectiu del qual és buscar solucions comunes i representatives donades unes preferències d'un grup d'experts definit, sobre un conjunt d'alternatives i en relació a un conjunt coherent de criteris o atributs. L'objectiu d'aquesta tesis és contribuir específicament en l'àrea lingüística de MCGDM / MAGDM millorant les metodologies i marcs matemàtics existents amb l'objectiu de poder modelar qualsevol tipus de situació de presa de decisions en grup que impliqui multi-granularitat i raonament qualitatiu molt heterogeni entre el grup (ús d'etiquetes lingüístiques no balancejades). En concret, la tesis es basa en l'ús de l'eina dels hesitant fuzzy lingüístic term sets (HFLTSs) que fou introduïda per Rodriguez et al (2012) amb l'objectiu de permetre als experts poder donar opinions i preferències lingüístiques usant el seu llenguatge habitual (i no, números) capturant també la incertesa, ambigüitat i manca d'informació característica en aquest tipus de decisions. La majoria d'estructures matemàtiques existents basades en l'ús de HFLTSs en problemes de MCGDM/MAGDM fan la hipòtesis que tots els experts han d'expressar-se usant el mateix set d'etiquetes lingüístiques i/o bé el pes que cada expert dona a cadascuna de les etiquetes ha de ser el mateix. Aquests estructures no són suficientment flexibles per modelar situacions de GDM de multi-granularitat que també incloguin diversitat de raonament qualitatiu amb etiquetes lingüístiques no balancejades de forma simultània. En primer lloc, la present tesis desenvolupa un nou concepte, el perceptual-map, definit sobre l'estructura algebraica de HFLTSs no balancejats i introdueix una nova distància basada en aquesta mètrica. Aquesta distància és utilitzada per definir un centroide (opinió central) i una mesura de consens per a qualsevol situació de MAGDM que necessiti de l'ús d'un set d'etiquetes lingüístiques no balancejat. En segon lloc, una funció de transformació basada en el perceptual-map es defineix per tal de poder modelar simultàniament situacions lingüístiques amb multi-granularitat i poder així, realitzar operacions en un espai projectat. A nivell pràctic, la tesis presenta dos aplicacions reals per demostrar la utilitat i rellevància de les eines matemàtiques desenvolupades. D'una banda, la tesis introdueix un nou mètode de classificació i rànquing, que és aplicat en l'àmbit de l'educació. El nou mètode és utilitzat per classificar i ranquejar els alumnes de secundària de l'escola Andorrana d'acord amb el seu grau de desenvolupament de la competència emprenedora. D'altra banda, s'ha desenvolupat un nou model de TOPSIS anomenat fuzzy multi-perceptual lingüístic TOPSIS, que s'ha aplicat en el context d'avaluació de smart cities. La nova versió de TOPSIS s'ha aplicat amb èxit per avaluar els criteris que governen la decisió estratègica de localització, en el context de ciutats europees, de les multinacionals del sector energètic.
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Umer, Adil. "Sustainability evaluation of transportation infrastructure under uncertainty : a fuzzy-based approach." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/53035.
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The construction and maintenance of transportation infrastructure consume significant natural resources, produces considerable waste and uses extensive human capital. Sustainability evaluation of alternative initiatives and policies for developing transportation infrastructure enables decision makers to make informed choices. Despite the availability of numerous sustainability rating tools for roadway infrastructure, there is a need to develop customizable sustainability evaluation tools for informed decision-making. Such tools, unlike the rating systems, ideally need to handle uncertain data, incorporate expert opinion and adapt to project and geographic specific constraints. Deterministic approaches for life cycle cost analysis (LCCA) and life cycle assessment (LCA) have been extensively applied to select sustainable pavement alternatives. However, the information used to conduct LCCA and LCA is often imprecise and vague in early project phases. Therefore, certain technique is required to incorporate and propagate such uncertainties so that the reliability of final results is transparent. Unlike probabilistic methods, fuzzy based techniques are more appropriate to handle uncertainties due to vagueness and imprecision in a computationally efficient manner. This study aimed to investigate the use of fuzzy logic to evaluate sustainability under uncertainty at two levels of infrastructures - Roadways as systems and pavements as components. A novel roadway sustainability evaluation framework was developed using indicators from existing green rating system. A customizable excel-based tool was programmed based on the framework to estimate the sustainability index (SI) of roadways under uncertainty using fuzzy synthetic evaluation (FSE) technique. The FSE technique enables the tool to evaluate reliable and informative SI by incorporating expert opinion. Moreover, fuzzy composite programming (FCP) technique was used to estimate the life cycle environmental and economic sustainability indices (SIs) from LCA and LCCA of pavement alternatives under uncertainty. The FCP technique improved the reliability of final results by propagating input uncertainties to the outputs. Scenario analysis was performed using FSE and FCP techniques to demonstrate the influence of uncertainties and decision maker’s preferences on the overall SI of roadways and pavements respectively. This study demonstrated a compelling utility of fuzzy-based techniques to evaluate sustainability under uncertainty in the early project phases for informed decision-making.Applied Science, Faculty of
Engineering, School of (Okanagan)
Graduate
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Kishk, Mohammed El-Said. "An integrated fuzzy approach to whole life costing based decision making." Thesis, Robert Gordon University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.369051.
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Wang, Ming-hua. "A knowledge-based system approach for project management decision-making support." Thesis, University of Warwick, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340476.
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Arslan, Dilek. "A Control System Using Behavior Hierarchies And Neuro-fuzzy Approach." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12605743/index.pdf.
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In agent based systems, especially in autonomous mobile robots, modelling the environment and its changes is a source of problems. It is not always possible to effectively model the uncertainity and the dynamic changes in complex, real-world domains. Control systems must be robust to changes and must be able to handle these uncertainties to overcome this problem. In this study, a reactive behaviour based agent control system is modelled and implemented. The control system is tested in a navigation task using an environment, which has randomly placed obstacles and a goal position to simulate an environment similar to an autonomous robot&rsquos indoor environment. Then the control system was extended to control an agent in a multi-agent environment. The main motivation of this study is to design a control system which is robust to errors and easy to modify. Behaviour based approach with the advantages of fuzzy reasoning systems is used in the system.
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Wirba, Elias Njoka. "An object-oriented knowledge-based systems approach to construction project control." Thesis, London South Bank University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336366.
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FILHO, ANTONIO CARLOS DE SOUZA SAMPAIO. "MODIFIED CAPITAL BUDGETING METHODS UNDER UNCERTAINTIES: AN APPROACH BASED ON FUZZY NUMBERS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=37098@1.
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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIROEssa tese apresenta uma abordagem alternativa para orçamento de capital, denominada Métodos Modificados de Avaliação de Projetos de Investimentos em Ambiente Fuzzy, para avaliação de projetos em condições de incerteza. O desenvolvimento da abordagem proposta está dividido em duas fases: na primeira fase, é estabelecido um modelo determinístico generalizado que prevê explicitamente a utilização dos custos de oportunidade associados com os fluxos de caixa intermediários de um projeto de investimento empresarial. Os pressupostos implícitos dos métodos modificados da taxa interna de retorno e do valor presente líquido são incluídos nos métodos do índice de lucratividade e do tempo de retorno do investimento total. Os indicadores resultantes são o índice de lucratividade modificado e o tempo de retorno do investimento modificado. Essa abordagem unificada tem a propriedade de coincidir as decisões de aceitação / rejeição de projetos de investimentos de mesmos horizontes de vida e escalas com as do valor presente líquido modificado e, portanto, maximizam a riqueza do acionista. Na segunda fase, números fuzzy triangulares são utilizados para representar as incertezas das variáveis de um projeto de investimento: os fluxos de caixa, as taxas de financiamento e de reinvestimento e a taxa de desconto ajustada ao risco. Os indicadores fuzzy resultantes são o valor presente líquido modificado, a taxa interna de retorno modificada, o índice de lucratividade modificado e o tempo de retorno do investimento modificado. A aplicação de custos de oportunidades e de critérios difusos para a atribuição dos valores das variáveis permite obter resultados mais realistas e compatíveis com as condições de mercado. Devido à complexidade dos cálculos envolvidos, novas funções financeiras de uso amigável são desenvolvidas utilizando Visual Basic for Applications do MS-Excel: três, para avaliação de projetos em condições de certeza (MVPL, MIL e MTRI) e quatro para avaliação em condições de incerteza (MVPLfuzzy, MTIRfuzzy, MILfuzzy e MTRIfuzzy). A principal contribuição dessa tese é a elaboração de uma nova abordagem unificada para orçamento de capital em condições de incerteza que enfatiza os pontos fortes dos métodos modificados do valor presente líquido e da taxa interna de retorno, enquanto contorna os conflitos e as desvantagens individuais dos métodos convencionais. Os resultados mostram que os métodos propostos são mais vantajosos e mais simples de se utilizar que outros métodos de avaliação de investimentos em condições de incerteza.
This thesis presents an alternative approach to capital budgeting, named Fuzzy Modified Methods of Capital Budgeting, for evaluating investment projects under uncertainties. The development of the proposed approach is divided into two phases: in the first stage, a general deterministic model that explicitly provides for the use of the opportunity costs associated with the interim cash flows of a project is established. The implicit assumptions of the modified internal rate of return and modified net present value methods are included in the index of profitability and in the total payback period. The resulting indicators are the modified index of profitability and the modified total payback period. This unified approach has the property to match the decisions of acceptance / rejection of investment projects with same horizons of life and same scales with the decisions of the modified net present value method and therefore maximize shareholder wealth. In the second phase, triangular fuzzy numbers are used to represent the uncertainties of the project variables: cash flows and reinvestment, financing and risk-adjusted discount rates. The resulting indicators are the fuzzy modified net present value, the fuzzy modified internal rate of return, the fuzzy modified index of profitability and the fuzzy modified total payback period. The application of opportunity costs and fuzzy criteria for determining the variables allows obtaining more realists and consistent results with the market conditions. Due to the complexity of the calculations involved, new MS-Excel financial functions are developed by using Visual Basic for Applications: three functions for evaluating projects under conditions of certainty (MVPL, MIL and MTRI) and four functions for evaluating projects under uncertainties (MVPLfuzzy, MTIRfuzzy, MILfuzzy and MTRIfuzzy). The main contribution of this thesis is to develop a unifying approach to capital budgeting under uncertainty that emphasizes the strengths of the methods of modified net present value and modified internal rate of return, while bypassing the individual conflicts and drawbacks of the conventional methods. Results show that the proposed methods are more advantageous and simpler to use than other methods of investment appraisal under uncertainties.
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Hosseini, Rahil. "Fuzzy based approach for modelling uncertainty in classification for a computer aided detection." Thesis, Kingston University, 2012. http://eprints.kingston.ac.uk/24621/.
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A computerized image analysis technology suffers from imperfection, imprecision and vagueness of the input data and its propagation to all individual components of the technology including image enhancement, segmentation and object classification. Furthermore, a computerized medical image analysis system (CMIAS) deals with another source of ambiguity that is inherent in the image-based practice of medicine and intuitive knowledge of experts. Therefore, a CMIAS such as computer aided detection (CAD) technologies implicitly suffer from uncertainty and vagueness both from image analysis techniques and medical diagnosis. Although several technology-oriented studies have been reported for CAD, no attempt has been made to address, model and overcome these types of uncertainty in the design of the CAD. However, uncertainty issues directly affect the accuracy of the system. This study addresses the main sources of the uncertainty in a CAD system. While uncertainty outcomes are latent in the input of a classifier, the aim is to model them in the classification for a CAD application. For this, this research takes advantages of type-2 fuzzy logic (T2FL). Integrating a T2FL model for object classification in CAD architecture allows us to model uncertainty issues. For this, an automatic approach models uncertainty in training dataset using membership function of a type-2 fuzzy set. This approach was applied to the candidate nodule classification problem in a lung CAD application. The ROC (receiver operating characteristic) analysis of the classifier results (with an average accuracy 95% (area under the ROC curve) for nodule classification) reveals that the T2FL is more capable of capturing the uncertainty in the model and achieving better performance results compared to type-l fuzzy logic counterpart. Furthermore, the research introduces the idea of uncertain rule-based pattern classification in environments which exhibit a lack of expert knowledge and with an imperfect training dataset. An automatic approach for rule extraction is presented which takes advantages of genetic algorithm for learning rule set of an T2FL system from training samples. The proposed approach was applied to the popular Wisconsin breast cancer diagnosis (WBCD) database. Analysis of the performance results reveals that this approach is competitive with, the best results of other proposed fuzzy classification methods to date in terms of trade-off between accuracy and interpretability, with an average accuracy of 96.6 % for the breast cancer diagnosis problem. This study introduces the concept of uncertainty in a CAD application. This is a first attempt toward modelling uncertainty issues in classification component for a CAD. The main contribution is automatically modelling uncertainties using membership functions and a rule set of a type-2 fuzzy logic. The performance evaluation on two different CAD classification problems (1) nodule classification in a lung CAD and (2) the WBCD diagnosis problem using Mammography CAD reveals the superiority of the T2FLS classifier for managing high levels of uncertainty compared to the T1FLS counterpart and providing classification that is more accurate. This approach is significant from two major aspects (l) clinical view: by producing more accurate results for diagnosis problems which can save more human lives, (2) technical view: modelling uncertainties in the design of a classifier using automatically presented approach for IT2FLS membership and rules generation. This is critical for multi-dimensional classification problems with large number of inputs and lack of expert knowledge as is the case for most of medical diagnosis problems.
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Soud, El Maameri Said David. "A fuzzy logic based approach to quality of service in 802.11b wireless networks." Thesis, London Metropolitan University, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.426490.
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Wu, Paul Horng Jyh, Jin Cheon Na, and Christopher S. G. Khoo. "A hybrid approach to fuzzy name search incorporating language-based and textbased principles." SAGE Publications, 2007. http://hdl.handle.net/10150/105835.
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Name Search is an important search function in various types of information retrieval systems, such as
online library catalogs and electronic yellow pages. It is also difficult due to the high degree of fuzziness
required in matching name variants. Previous approaches to name search systems use ad hoc
combinations of search heuristics. This paper first discusses two approaches to name modelingâ the
natural language processing (NLP) and the information retrieval (IR) modelsâ and proposes a hybrid
approach. The approach demonstrates a critical combination of complementary NLP and IR features that
produces more effective fuzzy name matching. Two principles, position-as-attribute and position-transitionlikelihood,
are introduced as the principles for integrating the advantageous aspects of both approaches.
They have been implemented in an NLP- and IR- hybrid model system called Friendly Name Search (FNS)
for real world applications in multilingual directory searches on the Singapore Yellow pages website.
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KOTHAMASU, RANGANATH. "INTELLIGENT CONDITION BASED MAINTENANCE - A SOFT COMPUTING APPROACH TO SYSTEM DIAGNOSIS AND PROGNOSIS." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1141339344.
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Lee, Keum-Chang. "Design of an intrusion detection system based on a fuzzy classification and voting approach." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.506587.
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Evans, Liam. "Experience-based decision support methodology for manufacturing technology selection : a fuzzy-decision-tree mining approach." Thesis, University of Nottingham, 2013. http://eprints.nottingham.ac.uk/13719/.
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Manufacturing companies must invest in new technologies and processes to succeed in a rapidly changing global environment. Managers have the difficulty of justifying capital investment in adopting new, state-of-the-art technology. Technology investment accounts for a large part of capital spending and is a key form of improving competitive advantage. Typical approaches focus on the expected return of investment and financial reward gained from the implementation of such equipment. With an increasingly dynamic market environment and global economic model, forecasting of financial payback can be argued to become increasingly less accurate. Subsequently, less quantifiable factors are becoming increasingly important. For example, the alignment of a technology with an organisations objective to fulfil future potential and gain competitive advantage is becoming as crucial as economic evaluation. In addition, the impact on human operators and skill level required must be considered. This research was motivated by the lack of decision methodologies that understand why a technology is more successful within an environment rather than re-examining the underlying performance attributes of a technology. The aim is to create a common approach where both experts and non-experts can use historical decision information to support the evaluation and selection of an optimal manufacturing technology. This form of approach is based on the logic in which a decision maker would irrationally recall previous decisions to identify relationships with new problem cases. The work investigates data mining and machine learning techniques to discover the underlying influences to improve technology selection under a set of dynamic factors. The approach initially discovers the practices to which an expert would conduct the selection of a manufacturing technology within industry. A defined understanding of the problem and techniques was subsequently concluded. This led to an understanding of the structure by which historical decision information is recalled by an expert to support new selection problems. The key attributes in the representation of a case were apparent and a form of characterising tangible and intangible variables was justified. This led to the development of a novel, experience-based manufacturing technology selection framework using fuzzy-decision-trees. The methodology is an iterative approach of learning from previously implemented technology cases. Rules and underlying knowledge of the relationships in past cases predicts the outcome of new decision problems. The link of information from a multitude of historical cases may identify those technologies with technical characteristics that perform optimally for projects with unique requirements. This also indicates the likeliness of technologies performing successfully based on the project requirements. Historical decision cases are represented through original project objectives, technical performance attributes of the chosen technology and judged project performance. The framework was shown to provide a comprehensive foundation for decision support that reduces the uncertainty and subjective influence within the selection process. The model was developed with industrial guidance to represent the actions of a manufacturing expert. The performance of the tool was measured by industrial experts. The approach was found to represent well the decision logic of a human expert based on their developed experience through cases. The application to an industrial decision case study demonstrated encouraging results and use by decision makers feasible. The model reduces the subjectivity in the process by using case information that is formed from multiple experts of a prior decision case. The model is applied in a shorter time period than existing practices and the ranking of potential solutions is well aligned to the understanding of a decision maker. To summarise, this research highlights the importance of focusing on less quantifiable factors and the performance of a technology to a specific problem/environment. The arrangement of case information thus represents the experience an expert would acquire and recall as part of the decision process.
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Javed, Kamran. "A robust & reliable Data-driven prognostics approach based on extreme learning machine and fuzzy clustering." Phd thesis, Université de Franche-Comté, 2014. http://tel.archives-ouvertes.fr/tel-01025295.
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Le Pronostic et l'étude de l'état de santé (en anglais Prognostics and Health Management (PHM)) vise à étendre le cycle de vie d'un actif physique, tout en réduisant les coûts d'exploitation et de maintenance. Pour cette raison, le pronostic est considéré comme un processus clé avec des capacités de prédictions. En effet, des estimations précises de la durée de vie avant défaillance d'un équipement, Remaining Useful Life (RUL), permettent de mieux définir un plan d'actions visant à accroître la sécurité, réduire les temps d'arrêt, assurer l'achèvement de la mission et l'efficacité de la production. Des études récentes montrent que les approches guidées par les données sont de plus en plus appliquées pour le pronostic de défaillance. Elles peuvent être considérées comme des modèles de type " boite noire " pour l'étude du comportement du système directement à partir des données de surveillance d'état, pour définir l'état actuel du system et prédire la progression future de défauts. Cependant, l'approximation du comportement des machines critiques est une tâche difficile qui peut entraîner des mauvais pronostics. Pour la compréhension de la modélisation de pronostic guidé par les données, on considère les points suivants. 1) Comment traiter les données brutes de surveillance pour obtenir des caractéristiques appropriées reflétant l'évolution de la dégradation ? 2) Comment distinguer les états de dégradation et définir des critères de défaillance (qui peuvent varier d'un cas à un autre)? 3) Comment être sûr que les modèles définis seront assez robustes pour montrer une performance stable avec des entrées incertaines s'écartant des expériences acquises, et seront suffisamment fiables pour intégrer des données inconnues (c'est à dire les conditions de fonctionnement, les variations de l'ingénierie, etc.)? 4) Comment réaliser facilement une intégration sous des contraintes et des exigences industrielles? Ces questions sont des problèmes abordés dans cette thèse. Elles ont conduit à développer une nouvelle approche allant au-delà des limites des méthodes classiques de pronostic guidé par les données. Les principales contributions sont les suivantes. - L'étape de traitement des données est améliorée par l'introduction d'une nouvelle approche d'extraction des caractéristiques à l'aide de fonctions trigonométriques et cumulatives qui sont basées sur trois caractéristiques : la monotonie, la "trendability" et la prévisibilité. L'idée principale de ce développement est de transformer les données brutes en indicateur qui améliorent la précision des prévisions à long terme.
- Pour tenir compte de la robustesse, la fiabilité et l'applicabilité, un nouvel algorithme de prédiction est proposé: Summation Wavelet-Extreme Learning Machine (SWELM). Le SW-ELM assure de bonnes performances de prédiction, tout en réduisant le temps d'apprentissage. Un ensemble de SW-ELM est également proposé pour quantifier l'incertitude et améliorer la précision des estimations.
- Les performances du pronostic sont également renforcées grâce à la proposition d'un nouvel algorithme d'évaluation de la santé: Subtractive-Maximum Entropy Fuzzy Clustering (S-MEFC). S-MEFC est une approche de classification non supervisée qui utilise l'inférence de l'entropie maximale pour représenter l'incertitude de données multidimensionnelles. Elle peut automatiquement déterminer le nombre d'états, sans intervention humaine.
- Le modèle de pronostic final est obtenu en intégrant le SW-ELM et le S-MEFC pour montrer l'évolution de la dégradation de la machine avec des prédictions simultanées et l'estimation d'états discrets. Ce programme permet également de définir dynamiquement les seuils de défaillance et d'estimer le RUL des machines surveillées. Les développements sont validés sur des données réelles à partir de trois plates-formes expérimentales: PRONOSTIA FEMTO-ST (banc d'essai des roulements), CNC SIMTech (Les fraises d'usinage), C-MAPSS NASA (turboréacteurs) et d'autres données de référence. En raison de la nature réaliste de la stratégie d'estimation du RUL proposée, des résultats très prometteurs sont atteints. Toutefois, la perspective principale de ce travail est d'améliorer la fiabilité du modèle de pronostic.
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William, Luo. "A Constraint-based Approach to Fuzzy Control." 1997. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0009-0112200611290704.
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Chien-Tsun, Liu, and 劉建村. "A Data-Gap-Based Fuzzy Clustering Approach." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/54469360085231506675.
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Su, Yi-Min SuYi-Min SuYi-Min, and 蘇益民. "Observer-based H∞ Fuzzy Control Design-Hybrid Taguchi-Genetic Fuzzy Control Approach." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/54025243201400377529.
Full textAbstract:
碩士國立高雄應用科技大學
電機工程系
99
This thesis deals with observer-based H∞ control problem for T-S fuzzy systems . By using Lyapunov stability analysis as the basis for derivation. literature on the observer-based control issue will encounter nonlinear matrix inequalities, must be solved by two-step procedure, lead to the solution set is too conservative. In this thesis, by using hybrid Taguchi genetic algorithm to search optimization controller gain and observer gain, the unknown controller gain and observer gain is assumed as nonvariable, the nonlinear matrix inequalities into linear matrix inequalities, avoid the shortcomings of two-step procedure.
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Lin, Yu-Tse, and 林雨澤. "SOS-based Fuzzy Controller Design - Homogeneous Polynomial Approach." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/8u9rk3.
Full textAbstract:
碩士國立中央大學
機械工程學系
102
In this thesis,it is mainly to research the non-quadratic stability conditions of continuous and discrete-time fuzzy systems.Extension of the state dependent Riccati inequalities to non-quadratic Lyapunov function of the form V (x) = 1/2x′Q−1(x)x.For the continuous case,it will produce the derivative term of Q(x)from V(x) for differential t,in order to avoid this problem,we will reference Euler homogeneous polynomial theorem,using the theorem to detect its stability conditions of fuzzy systems, and use the modeling techniques Taylor series,then test it with the method of sum of squares to determine the stability.For the discrete-time case,also reference Euler homogeneous polynomial theorem,determining the stability with the method of sum of squares.Lastly, examples of polynomial fuzzy systems are demonstrated to show the proposed method being effective.
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Wang, Yu-Sheng, and 王裕盛. "Model-Based Fault Diagnosis Using Fuzzy-Logic Approach." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/54987477215423453817.
Full textAbstract:
碩士中華大學
電機工程學系碩士班
87
Associated with an increasing demand for higher performance as well as for more safety and reliability of systems, fault diagnosis has received more and more attention. One area of active research is model-based fault detection combining rule-based diagnosis. In the pass two decade, many researchers have developed many techniques for model-based fault detection to improve the performance of fault detection. Among these techniques, observer-based and parity equation methods are the focus of the methodology. In rule-based diagnosis, fuzzy system is the nature tool. though it has an important issue that the rule acquision is tedious. In this thesis, the concept of observer-based and parity equation methods will be presented. Then, an efficient isolation will be proposed with a decomposition method and demonstrate the procedure with an simulation of MIMO system. The goal of a decomposition method is to extract thresholds from residuals owning multiple thresholds. In diagnosis, fuzzy system is adopted to residual evaluation and the decision maker is the binary logic. Fuzzy threshold can reduce the effect of uncertainty through fuzzy-logic system. Due to the decomposition procedure, the rules of fuzzy system can establish clearly and easily. In the meantimes, we just tackle the single fault being bias fault and have good results. Furthermore, the control loop covering the fault effect will be discussed.
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Lopes, Nuno Manuel Lucas Vieira. "Fuzzy logic based approach for object feature tracking." Doctoral thesis, 2016. http://hdl.handle.net/10348/5345.
Full textAbstract:
Tese de Doutoramento em Engenharia Electrotécnica e de ComputadoresNesta tese é introduzida uma nova técnica de seguimento de pontos característicos de objectos em sequências de imagens em escala de cinzentos baseada em lógica difusa. É apresentada uma metodologia versátil e modular para o seguimento de objectos utilizando conjuntos difusos e motores de inferência. É também apresentada uma extensão desta metodologia para o correcto seguimento de múltiplos pontos característicos. Para se realizar o seguimento são definidas inicialmente três funções de pertença. Uma função de pertença está relacionada com a propriedade distintiva do objecto que desejamos seguir, outra está relacionada com o facto de se considerar que o objecto tem uma movimentação suave entre cada imagem da sequência e outra função de pertença referente à sua previsível localização futura. Aplicando estas funções de pertença aos píxeis da imagem, obtêm-se os correspondentes conjuntos difusos, que serão manipulados matematicamente e servirão como entrada num motor de inferência. Situações como a oclusão ou falha na detecção dos pontos característicos são ultrapassadas utilizando posições estimadas calculadas a partir do modelo de movimento e a um vector de estados do objecto. Esta metodologia foi inicialmente aplicada no seguimento de um objecto assinalado pelo utilizador. Foram realizados vários testes de desempenho em sequências de imagens sintéticas e também reais. Os resultados experimentais obtidos são apresentados, analisados e discutidos. Embora esta metodologia pudesse ser aplicada directamente ao seguimento de múltiplos pontos característicos, foi desenvolvida uma extensão desta metodologia para esse fim. Nesta nova metodologia a sequência de processamento de cada ponto característico é dinâmica e hierárquica. Dinâmica por ser variável ao longo do tempo e hierárquica por existir uma hierarquia de prioridades relativamente aos pontos característicos a serem seguidos e que determina a ordem pela qual esses pontos são processados. Desta forma, o processo dá preferência a pontos característicos cuja localização é mais fácil de prever comparativamente a pontos característicos cujo conhecimento do seu comportamento seja menos previsível. Quando esse valor de prioridade se torna demasiado baixo, esse ponto característico deixa de ser seguido pelo algoritmo. Para se observar o desempenho desta nova abordagem foram utilizadas sequências de imagens onde várias características indicadas pelo utilizador são seguidas. Na parte final deste trabalho são apresentadas as conclusões resultantes a partir do desenvolvimento deste trabalho, bem como a definição de algumas linhas de investigação futura.
This thesis introduces a novel technique for feature tracking in sequences of greyscale images based on fuzzy logic. A versatile and modular methodology for feature tracking using fuzzy sets and inference engines is presented. Moreover, an extension of this methodology to perform the correct tracking of multiple features is also presented. To perform feature tracking three membership functions are initially defined. A membership function related to the distinctive property of the feature to be tracked. A membership function is related to the fact of considering that the feature has smooth movement between each image sequence and a membership function concerns its expected future location. Applying these functions to the image pixels, the corresponding fuzzy sets are obtained and then mathematically manipulated to serve as input to an inference engine. Situations such as occlusion or detection failure of features are overcome using estimated positions calculated using a motion model and a state vector of the feature. This methodology was previously applied to track a single feature identified by the user. Several performance tests were conducted on sequences of both synthetic and real images. Experimental results are presented, analysed and discussed. Although this methodology could be applied directly to multiple feature tracking, an extension of this methodology has been developed within that purpose. In this new method, the processing sequence of each feature is dynamic and hierarchical. Dynamic because this sequence can change over time and hierarchical because features with higher priority will be processed first. Thus, the process gives preference to features whose location are easier to predict compared with features whose knowledge of their behavior is less predictable. When this priority value becomes too low, the feature will no longer tracked by the algorithm. To access the performance of this new approach, sequences of images where several features specified by the user are to be tracked were used. In the final part of this work, conclusions drawn from this work as well as the definition of some guidelines for future research are presented.
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Lopes, Nuno Vieira. "Fuzzy logic based approach for object feature tracking." Doctoral thesis, 2012. http://hdl.handle.net/10400.8/3259.
Full textAbstract:
This thesis introduces a novel technique for feature tracking in sequences of
greyscale images based on fuzzy logic. A versatile and modular methodology
for feature tracking using fuzzy sets and inference engines is presented.
Moreover, an extension of this methodology to perform the correct tracking
of multiple features is also presented.
To perform feature tracking three membership functions are initially
defined. A membership function related to the distinctive property of the feature
to be tracked. A membership function is related to the fact of considering
that the feature has smooth movement between each image sequence and a
membership function concerns its expected future location. Applying these
functions to the image pixels, the corresponding fuzzy sets are obtained and
then mathematically manipulated to serve as input to an inference engine.
Situations such as occlusion or detection failure of features are overcome
using estimated positions calculated using a motion model and a state vector
of the feature.
This methodology was previously applied to track a single feature identified
by the user. Several performance tests were conducted on sequences of
both synthetic and real images. Experimental results are presented, analysed
and discussed. Although this methodology could be applied directly to multiple
feature tracking, an extension of this methodology has been developed
within that purpose. In this new method, the processing sequence of each
feature is dynamic and hierarchical. Dynamic because this sequence can
change over time and hierarchical because features with higher priority will
be processed first. Thus, the process gives preference to features whose location
are easier to predict compared with features whose knowledge of their
behavior is less predictable. When this priority value becomes too low, the
feature will no longer tracked by the algorithm. To access the performance
of this new approach, sequences of images where several features specified
by the user are to be tracked were used.
In the final part of this work, conclusions drawn from this work as well as
the definition of some guidelines for future research are presented.Nesta tese é introduzida uma nova técnica de seguimento de pontos característicos de objectos em sequências de imagens em escala de cinzentos baseada em lógica difusa. É apresentada uma metodologia versátil e modular para o seguimento de objectos utilizando conjuntos difusos e motores de inferência. É também apresentada uma extensão desta metodologia para o correcto seguimento de múltiplos pontos característicos. Para se realizar o seguimento são definidas inicialmente três funções de pertença. Uma função de pertença está relacionada com a propriedade distintiva do objecto que desejamos seguir, outra está relacionada com o facto de se considerar que o objecto tem uma movimentação suave entre cada imagem da sequência e outra função de pertença referente à sua previsível localização futura. Aplicando estas funções de pertença aos píxeis da imagem, obtêm-se os correspondentes conjuntos difusos, que serão manipulados matematicamente e servirão como entrada num motor de inferência. Situações como a oclusão ou falha na detecção dos pontos característicos são ultrapassadas utilizando posições estimadas calculadas a partir do modelo de movimento e a um vector de estados do objecto. Esta metodologia foi inicialmente aplicada no seguimento de um objecto assinalado pelo utilizador. Foram realizados vários testes de desempenho em sequências de imagens sintéticas e também reais. Os resultados experimentais obtidos são apresentados, analisados e discutidos. Embora esta metodologia pudesse ser aplicada directamente ao seguimento de múltiplos pontos característicos, foi desenvolvida uma extensão desta metodologia para esse fim. Nesta nova metodologia a sequência de processamento de cada ponto característico é dinâmica e hierárquica. Dinâmica por ser variável ao longo do tempo e hierárquica por existir uma hierarquia de prioridades relativamente aos pontos característicos a serem seguidos e que determina a ordem pela qual esses pontos são processados. Desta forma, o processo dá preferência a pontos característicos cuja localização é mais fácil de prever comparativamente a pontos característicos cujo conhecimento do seu comportamento seja menos previsível. Quando esse valor de prioridade se torna demasiado baixo, esse ponto característico deixa de ser seguido pelo algoritmo. Para se observar o desempenho desta nova abordagem foram utilizadas sequências de imagens onde várias características indicadas pelo utilizador são seguidas. Na parte final deste trabalho são apresentadas as conclusões resultantes a partir do desenvolvimento deste trabalho, bem como a definição de algumas linhas de investigação futura.
27
Chang, Wei-sheng, and 章為盛. "SOS-Based Fuzzy Observer Dsigns -Homogeneous Polynomial Approach." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/48766944955924484172.
Full textAbstract:
碩士國立中央大學
機械工程學系
102
In this thesis, we extend of the state dependent Riccati inequalities to non-quadratic Lyapunov function of the form V (e) = 1/2e^TQ(e)e where Q(e) > 0 requires that Q(e) is a gradient of positive definite function.Unfortunately, the test of Q(e) is nonconvex problem. Thus this thesis studies stabilization problems of the polynomial fuzzy systems via homogeneous Lyapunov functions exploiting the Euler’s homogeneity property to construct a family of SOS polynomials that solves the nonconvexity problem and releases conservatism as well. Lastly, examples of polynomial fuzzy systems are demonstrated to show the proposed method being effective and effective.
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Shih, Chih-Yuan, and 石智源. "A Fuzzy MADM Approach for the BSC-Based Performance." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/65089568715239343289.
Full textAbstract:
碩士國立雲林科技大學
企業管理系碩士班
92
This paper uses the fuzzy multiple attribute decision making (FMADM) method on BSC-Based Performance. There will be many vague, uncertainly and unstructured problems encountered in the evaluation of BSC non-formulaic Key Performance Indicators(KPIs) on non-financial perspectives. This paper using FMADM method try to solve the situation. In the evaluation process the judges can measure different weighting KPIs by fuzzy linguistic terms. By this way collects many individual’s pectoral opinions to transform into a impersonal opinion to evaluation BSC-Based performance. Finally, empirical results is shown to highlight the procedure of the proposed method. This method is not only efficiently handles vague or ill-defined information, but can also be easily used by most real-world decision making processes. It is also contributive for evaluation BSC-Based performance.
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Shieh, Horng-Lin, and 謝鴻琳. "A Fuzzy-Clustering-Based Robust Approach of Function Approximation." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/3ed32p.
Full textAbstract:
博士國立臺灣科技大學
電機工程系
94
In recent scientific applications, modeling an unknown system is a very important research subject. However, it is difficult to well model a system by mathematics model when the acquired system knowledge is incomplete, linguistic interpretations or expertise descriptions by experts. It had been no breakthrough on solving the problem until the proposal of fuzzy theory that has become one of efficient methodologies on system modeling. Due to influence of various factors, the sampling data used for system modeling often include noises and outliers. If such sampling data is directly being used to model a system, there will be a big difference, named overfitting, on the system behavior between the resultant modeled system and the actual system. To overcome this problem, this thesis presents an unsupervised fuzzy model construction approach to extracting fuzzy rules directly from numerical input–output sampling data for nonlinear systems bound with noises and outliers. There are two core ideas in the proposed method: (1) The robust fuzzy c-means (RFCM) algorithm is proposed to greatly mitigate the influence of data noises and outliers; and (2) A fuzzy-based data sifter (FDS) is proposed to locate good turning-points to partition a given nonlinear data domain into piecewise clusters so that a Takagi and Sugeno fuzzy model can be constructed with fewer rules and less errors. Several experiments are illustrated and their results have shown the proposed approach has good performance in various kinds of data domains with data noises and outliers
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劉翔銘. "A Novel Approach for Verifying Fuzzy Knowledge-Based Systems." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/67146310474886838366.
Full textAbstract:
碩士國立暨南國際大學
資訊管理學系
92
As domain knowledge has played an important role for various applications, such as the development of intelligent systems and decision-support systems, the issues concerning knowledge acquisition and knowledge management have attracted researchers from many fields. Furthermore, the lead-in of fuzzy theory has strengthened the power of knowledge representation and reasoning in expert systems. While collecting knowledge from multiple knowledge sources, a variety of logical problems might occur, e.g., redundancy, conflict, circularity and incompleteness, which will lead to incorrect decisions and affect the efficiency of the expert systems. In this thesis, we attempt to detect potential anomalies among fuzzy rules by proposing a fuzzy rule verification algorithm. Moreover, a fuzzy knowledge base verification system has been developed based on the novel approach. Keyword: artificial intelligence, expert systems, knowledge verification, fuzzy logic, knowledge base
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Chen, Shih-lun, and 陳仕倫. "Fuzzy Dynamic Systems Control via Piecewise Region-based Approach." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/61532242491710025555.
Full textAbstract:
碩士國立中央大學
電機工程研究所
95
The stability analysis and controller design for T-S fuzzy system are discussed in this thesis. This thesis combines the fuzzy region concept and the piecewise Lyapunov stability criterion to design a new fuzzy controller which is called piecewise T-S fuzzy region controller. We utilized the fuzzy region concept to partition the original plant rules into several fuzzy regions. Only one partial fuzzy region is fired at the instant of each input vector being coming. The original controller design of T-S fuzzy region concept is required to find the common Lyapunov matrix. This common positive definite matrix may not exist when the T-S fuzzy system includes many fuzzy rules. Now we utilize the piecewise Lyapunov stability criterion and the regional concept to find the individual Lyapunov matrix and fuzzy controller for each region. Therefore, this proposed condition is less conservative. The piecewise Lyapunov stability criterion is expressed in terms of Linear Matrix Inequalities (LMIs). The advantages of the piecewise fuzzy regional concept are less conservative, simpler to design and to be realized easily. In addition, this thesis will extend the piecewise Lyapunov stability criterion and the fuzzy region concept to the controller design of system with uncertainty and observer design. The purposes of this thesis are to address the robust fuzzy region control problem and design the observer rules for each fuzzy region such that the overall fuzzy model are stabilized. Furthermore, numerically examples are given to illustrate the usefulness of the proposed approaches.
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Wu, Fong Hsiang, and 吳逢祥. "Adaptive RSVP Buffer Control Based on Neuro-Fuzzy Approach." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/48691430713152583741.
Full textAbstract:
碩士國立成功大學
資訊工程研究所
87
This thesis proposes an adaptive RSVP buffer control scheme based on the neuro-fuzzy approach that is called RSVP Neuro-Fuzzy Buffer Control Scheme (RSVP-NFBCS). The RSVP-NFBCS controls the occupancy of the buffer by dynamically allocating bandwidth, so that it can not only to prevent the buffer from overflow and underflow but also improve the utilization of reserved bandwidth effectively. The RSVP-NFBCS is constructed by using a fuzzy neural network model with an additional reference model which is called Fuzzy Rule Generator (FRG). The FRG adaptively extracts from the training patterns fuzzy rules by the back-propagation learning algorithm with momentum (BPM). There are two different operation modes in RSVP-NFBCS; inference mode and learning mode. In the inference mode, the RSVP-NFBCS infers the required token rate by the learned fuzzy rules. In learning mode, the reference module FRG adopts BPM to learn the new fuzzy rules. In summary, the RSVP-NFBCS has advantages of adaptive fuzzy rule learning ability. According to the simulation results, the proposed RSVP-NFBC has a good performance of buffer control in both VBR traffic and CBR traffic.
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Shu, Sue-Ping, and 徐淑平. "A New Approach to Fuzzy Logic Inference Based on Spatial Relationship of Fuzzy Subsets." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/27666831959965319502.
Full textAbstract:
碩士國立清華大學
資訊科學研究所
84
In the study, a new inference method which match the intuition of humanreasoning i sproposed. For each fuzy rule "if x is A, then y is B" the roposed inference method produces an output fuzzy subset B' according to the spatial relationship of input fuzzy subset A' and the antecedent A. The membership function of the output fuzzy subset B' is of the same shape as that of the input fuzzy subset A'. Moreover, the spatial relationship of B' is consistent with that of A and A'. Experiments on a simple single-input single-output example and the truck backer-parking control are performed to compare our proposed inference method with the Mamdani's method. These experiments show that our results compares favorably with that of the Mamdani's method.
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B, G. MANJULA. "POWER SYSTEM CONTINGENCY RANKING ADOPTING CONVENTIONAL & FUZZY BASED APPROACH." Thesis, 2011. http://dspace.dtu.ac.in:8080/jspui/handle/repository/13868.
Full textAbstract:
M.TECHThe reliability of power system is generally judged in terms of systems adequacy & security. The term adequacy refers to systems capacity to meet the load demand within the component ratings & voltage limits at any time. The term security refers to system’s ability to withstand the impact of sudden changes due to equipment outage, such as the loss of a generator, transmission line etc. The security of the system is defined in terms of list of contingencies (i.e. transmission line & generator outages) which may cause insecure operation. Clearly as the system conditions changes, this list changes. In order to determine the list of contingencies, load flow analysis should be performed to determine the impact of each contingency on the system performance. However since this is not computationally feasible for on line applications, contingency selection algorithms have been developed to identify the set of contingencies which may create problems. A scalar function called performance index is used in the calculation of the contingency ranking. A real power & voltage performance index is calculated which evaluates the severity of contingency derived from the current overload of lines. The operating state of the power system is a function of time. It keeps on changing due to variation in load level at various buses or due to rescheduling of generation. To keep the system secure, it is imperative to know the impact of unplanned outages in advance so that suitable preventive / control measures can be taken if necessary. In deregulated operating regime - 8 - power system security is an issue that needs due consideration from researchers. Real power & voltage contingency ranking is an integral part of security assessment. The objective of contingency screening & ranking is to quickly & accurately short list critical contingencies from a large list of credible contingencies & rank them according to their severity for further rigorous analysis. A performance index is computed for each single line contingency. To obtain the magnitudes of various parameters a MI power computer aided power system study software which employ iterative methods are used. This document presents an approach using fuzzy logic to evaluate the degree of severity of the considered contingency & to eliminate the masking effect in the Technique. Contingency Ranking considering transmission line & generator outages are tabulated for five bus & IEEE Fourteen bus systems. Then fuzzy logic is developed to unmask the severity between the two conventionally calculated contingency rankings.
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"A BEHAVIOR BASED ROBOT CONTROL SYSTEM USING NEURO-FUZZY APPROACH." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/109765/index.pdf.
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Beg, Mirza Tariq. "QoS Routing for computer networks-A fuzzy logic based approach." Thesis, 2001. http://hdl.handle.net/2009/889.
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Tzeng, Jing-he, and 曾景禾. "Synchronization of Chaotic Systems with Fuzzy Observer:Neural-Network-Based Approach." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/20696490751551564987.
Full textAbstract:
碩士國立臺南大學
電機工程研究所
98
This study presents an effective approach for the exponential synchronization of chaotic systems with external disturbance. In order to estimate the states of master system, a fuzzy observer is proposed to realize the synchronization [1]. First, a neural-network (NN) model is used to represent the chaotic system. Then, a linear differential inclusion (LDI) state-space representation is established for the dynamics the NN model. Based on the LDI state-space representation, a stability criterion of error dynamics derived in terms of Lyapunov’s direct method is proposed to ensure that the trajectories of the slave system can approach those of master system. Subsequently, the stability condition of this criterion is reformulated into a linear matrix inequality (LMI). According to the LMI, a fuzzy observer is then synthesized to guarantee that the error dynamics is asymptotically stable. Finally, a numerical example with simulations is given to illustrate the concepts discussed throughout this study.
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Lan, Wan-Ping, and 藍琬萍. "The ELECTRE Multicriteria Analysis Approach Based on Intuitionistic Fuzzy Sets." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/02944541552728638446.
Full textAbstract:
碩士長庚大學
企業管理研究所
99
Nowadays, a large number of questions about multiple criteria have been concerned in present societal economy. Thus, in order to meet the multiple criteria questions in the world, people proceeded to focused on the more complex question in decision making. Because of these, the multiple criteria decisions making become a important research in the present. The fuzzy set uses the membership degree and non-membership degree to express some unable definitions of funny concepts and make the total sum of them is 1. The fuzzy set has many developments and the most important one is the Intuitionistic Fuzzy Sets. Intuitionistic Fuzzy Sets was in the basis of the fuzzy set, and bring up the degree of hesitancy to make the total sum of the membership degree and non-membership degree does not equate 1. The intuitionistic fuzzy Sets applied into some different decision methods, include estimative function, the partial relationship of intuitionistic fuzzy, colony decision, the TOPSIS, simple weighted method, and analytic hierarchy process in the past. But there has few researches combine the superiority sort method that is important and applied to many different regions of the MADM with the intuitionistic fuzzy sets. This research develops a diverse ELECTRE that is adding intuitionistic fuzzy sets for solving the uncertain problems of decision data. In the multiple criteria decision making, the ELECTRE is the important advantage sort method and use in different regions that could assist the policymaker to find out the best case or the case sort. The ELECTRE is very sensitive in the threshold value, this research raises the decision quality as develops three new ELECTRE methods which applied the intuitionistic fuzzy sets that can solve the uncertain problems. This research develops three different ELECTRE methods. The first method is using score function and accuracy function to establish concordances and disconcordances. The second method is using containing relation of the intuitionistic fuzzy sets to compatibility sets, weak compatibility sets, discordant sets, weak discordant sets, and non-differentiation. The third method is adding the conception of the degree of hesitancy ( ) to strong, middle and weak compatibility sets, strong, middle and weak discordant sets, and discordant sets. The empirical method of this research is questionnaire that the examinees sort when they decide to consume chocolate and digital camera. This research discovers the structure model of the ELECTRE doesn't suit the sensitive products, such as chocolate products. The third method is the best method as the sort correlation between the examinees matches pretty well and the first and second methods also have middle relation, hence the new three development methods of this research are all feasibility. Keyword: Multicriteria Analysis, Intuitionistic Fuzzy Sets, ELECTRE, TOPSIS
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Swain, D. P. "Supplier selection in risk consideration: a fuzzy based topsis approach." Thesis, 2014. http://ethesis.nitrkl.ac.in/5870/1/E-69.pdf.
Full textAbstract:
Supplier selection, the process of finding the right suppliers who are able to provide the buyer with the right quality products and/or services at the right price, at the right time and in the right quantities, is one of the most critical activities for establishing an effective supply chain. In classical Multi-Criteria Decision Making (MCDM) methods, the ratings and the weights of the criteria are known precisely. Owning to vagueness of the decision data, the crisp data are inadequate for real-life situations. Since human judgments including preferences are often vague and cannot be expressed by exact numerical values, the application of fuzzy concepts in decision making is deemed to be relevant. On the other hand, it is a hard problem since supplier selection is typically a MCDM problem involving several conflicting criteria on which decision maker’s knowledge is usually vague and imprecise. In the present work, a risk-based suppliers’ evaluation module is proposed. Linguistic values are used to assess the ratings and weights for the risk based supplier selection factors. These linguistic ratings can be expressed in triangular fuzzy numbers. Then, a hierarchy MCDM model based on fuzzy-sets theory is proposed to deal with the supplier selection problems in the supply chain system. According to the concept of the fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), a closeness coefficient is defined to determine the ranking order of all suppliers by calculating the both fuzzy positive-ideal solution and fuzzy negative-ideal solution, simultaneously. Empirical data have been analysed and results obtained thereof, have been reported to exhibit application potential of the decision-support systems in appropriate situation.
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Shi, Yu-Zhou, and 施毓洲. "A T-S Fuzzy Modeling Approach Based on Interval Type-2 Fuzzy C-means Algorithm." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/w32vgf.
Full textAbstract:
碩士國立臺北科技大學
自動化科技研究所
102
This paper presents a T-S fuzzy modeling approach based on interval Type-2 fuzzy C-means algorithm. For fuzzy modeling, the first step is to determine the number of fuzzy rules. For this reason, fuzzy interval Type-2 C-means algorithm is adopted to classify the data points and determine the numbers of the cluster. Based on Xie-Beni index criterion, by defining the cluster numbers as the rule number, and then the optimal fuzzy rule number can be determined. Moreover, fuzzy c-regression model (FCRM) algorithm is adopted to divide unknown system into several linear systems. Based on the input and output data, we can establish the fuzzy rule parameters initial values of each linear system. According to the parameter messages from the process, the orthogonal least squares method can obtain the optimal parameter values for linear systems. Therefore, the Takagi-Sugeno fuzzy model can be established. Finally, based on the conception of Type-2 fuzzy C-means algorithm, Xie-Beni index and FCRM, some examples are illustrated to demonstrate that the proposed modeling method can be better than the other existing methods.
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"Strategic groups: a resource-based view and neuro-fuzzy systems approach." Tese, MAXWELL, 2004. http://www.maxwell.lambda.ele.puc-rio.br/cgi-bin/db2www/PRG_0991.D2W/SHOW?Cont=5856:pt&Mat=&Sys=&Nr=&Fun=&CdLinPrg=pt.
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Liang, Mao-Cheng, and 梁茂成. "Fuzzy Control for Nonlinear Systems Based-on Linear Matrix Inequalities Approach." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/01602601295250607162.
Full textAbstract:
碩士國立交通大學
電機與控制工程系
91
The control of nonlinear systems is difficult because no systematic mathematical tools are available to find necessary and sufficient condition to guarantee their stability and no systematic methodology exists to design controller to ensure the closed-loop performance. The problem becomes yet more complex if some of the plant parameters are unknown. By using a Takagi-Sugeno (T-S) fuzzy plant model, a nonlinear system can be expressed as a weighted sum of some simple subsystems. This model provides a fixed structure to some nonlinear systems and facilitates the analysis of the systems. In this research, we have presented a linear controller design method for nonlinear plants with unknown parameters based on a LMI approach. The plant is represented by a TSK model. The parameters of the linear controller can be obtained by solving some LMIs. Our linear controller has a simpler structure than that of the fuzzy controller. Simulation results show that the proposed controller provides better performance than that of fuzzy controller.
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Lee, Yeong-Chyi, and 李詠騏. "A Genetic Fuzzy Knowledge Acquisition Strategy Based on the Michigan Approach." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/07455806266123995516.
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碩士義守大學
資訊工程學系
89
Developing an effective expert system requires constructing a complete, consistent, and unambiguous knowledge base. Conventional knowledge-acquisition approaches spend much interview time in building a knowledge base even though many tools have been developed to help with the acquisition process. In this thesis, we thus consider two kinds of knowledge acquisition problems — fuzzy knowledge integration problem and fuzzy knowledge discovery problem. In the first part of this thesis, we consider the fuzzy knowledge integration problem. Since knowledge required to develop a knowledge-based system is often distributed among groups of experts rather than being available from a single expert, integrating the knowledge in different sources is thus very time-consuming. We thus propose a fuzzy knowledge integration algorithm to generate a concise and accurate fuzzy knowledge base from different knowledge sources. The fuzzy rules from different sources are first collected to form a rule pool. These rules are then evaluated by three criteria including accuracy, utility and coverage. Three evaluation procedures, each for a criterion, are thus proposed. An integration algorithm and a set of test objects are used to select good rules to form the resulting knowledge base. The proposed algorithm can remove redundancy, subsumption and contradiction among rules. A concise and compact fuzzy rule base is thus constructed effectively without human expert intervention and thus save much time for knowledge integration. In the second part of this thesis, we consider the fuzzy knowledge discovery problem. A novel genetic fuzzy-rule learning algorithm based on the Michigan approach to automatically construct a fuzzy knowledge base is proposed. The proposed approach consists of three phases: fuzzy-rule generating, fuzzy-rule encoding and fuzzy-rule evolution. In the fuzzy-rule generating phase, a number of fuzzy rules are randomly generated. In the fuzzy-rule encoding phase, all the rules generated are translated into fixed-length bit strings to form an initial population. In the fuzzy-rule evolution phase, genetic operations and credit assignment are applied at the rule level. This phase chooses good individuals in the population for mating, gradually creating better offspring fuzzy rules. The evolution process is iteratively executed until a predefined number of generations is reached. The fuzzy rules in the last generation are then gathered together to form the resulting fuzzy rule base.
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Liu, Chong-Ping, and 劉聰平. "Tuning of PID Controllers Based on System Parameters:A Fuzzy Neural Approach." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/60798929258362479644.
Full textAbstract:
碩士國立交通大學
電機與控制工程系
88
In the thesis, we present a PID tuning method using the Fuzzy Neural Network (FNN) based on system parameters. PID tuning methods were widely used for stable processes, or some over-damped unstable processes. However, PID controller for under-damped unstable processes and higher order unstable processes is less common. An FNN approach is proposed to identify the relationship between system parameters and the PID controller parameters that meets the performance index. Then, the FNN is used to automatically tune the PID controller parameters for different system parameters so that neither numerical methods nor graphical methods need be used. Even though for some of the heavily oscillatory processes, the FNN still can find a suitable PID controller parameters. Simulation results show that the FNN can achieve the specified values efficiently.
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Liang, Chun-Kai, and 梁鈞凱. "Sensorless Vector Control of InductionMotors Based on T-S Fuzzy Approach." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/r35d73.
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碩士中原大學
電機工程研究所
91
By assuming known rotor resistance and load torque, this thesis presents a new design method for sensorless induction motors to achieve speed tracking. Our method is developed based on T-S fuzzy model. First, a T-S fuzzy observer is addressed to estimate the immeasurable states of rotor °ux and rotor speed. Then, the speed tracking controller is proposed by using the design concept of virtual desired variables. The resulting control gains are obtained by solving LMIs, whereby the stability is also proven. Finally, numerical simulations and practical experiments are found to be consistent with theoretical derivations.
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Liu, Chung-Chi, and 劉仲琦. "High-Order Weighted Fuzzy Time Series Based on Different Discretization Approach." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/20646934805818173430.
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碩士朝陽科技大學
資訊管理系碩士班
101
There are many uncertainty problems in the Human society, such as the forecasting of economic growth rate, financial crisis, etc. Since Song and Chissom proposed the concept of fuzzy time series in 1993, many scholars have proposed different models to deal with these problems. However, previous studies usually did not consider the transfer original data to the fuzzy linguistic value by the subjective opinions in fuzzy process, which cannot objectively show the characteristics of the data. Based on above concepts, the purpose of this study is to explore ways of determining the objective lengths of intervals and amount of linguistic in fuzzy time series. This study proposed a high-order weighted fuzzy time series model based on variable length discretization approach (VLDA) and N-th quantile discretization approach (NQDA) to make forecasts. In order to verify the proposed method, the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) from the Taiwan Stock Exchange Corporation are used in the experiment, and the experiment results are compared with other methods in with this study. The forecasting performance shows that the proposed method having better forecasting ability. An intelligent decision support system (DSS) for stock market will be developed in this study. It is supposed to be a useful decision support tools for the investor to make better trading strategies in the future stock market.
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Jadon, Rakesh Singh. "An evolutionary learning-based fuzzy theoretic approach for characterizing video sequences." Thesis, 2002. http://localhost:8080/iit/handle/2074/2215.
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Chenji, Jayanth Harshavardhan. "A Fuzzy Logic-Based Approach for Node Localization in Mobile Sensor Networks." 2009. http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7510.
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In most range-based localization methods, inferring distance from radio signal
strength using mathematical modeling becomes increasingly unreliable and complicated
in indoor and extreme environments, due to effects such as multipath propagation
and signal interference. We propose FuzLoc, a range-based, anchor-based,
fuzzy logic enabled system system for localization. Quantities like RSS and distance
are transformed into linguistic variables such as Low, Medium, High etc. by binning.
The location of the node is then solved for using a nonlinear system in the fuzzy
domain itself, which outputs the location of the node as a pair of fuzzy numbers. An
included destination prediction system activates when only one anchor is heard; it
localizes the node to an area. It accomplishes this using the theoretical construct of
virtual anchors, which are calculated when a single anchor is in the node’s vicinity.
The fuzzy logic system is trained during deployment itself so that it learns to
associate an RSS with a distance, and a set of distances to a probability vector.
We implement the method in a simulator and compare it against other methods like
MCL, Centroid and Amorphous. Extensive evaluation is done based on a variety of
metrics like anchor density, node density etc.
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Shalaby, Mohamed Ahmed Wahby. "Fingerprint Recognition: A Histogram Analysis Based Fuzzy C-Means Multilevel Structural Approach." Thesis, 2012. http://spectrum.library.concordia.ca/973951/1/Shalaby_PhD_S2012.pdf.
Full textAbstract:
In order to fight identity fraud, the use of a reliable personal identifier has become a necessity. Fingerprints are considered one of the best biometric measurements and are used as a universal personal identifier. There are two main phases in the recognition of personal identity using fingerprints: 1) extraction of suitable features of fingerprints, and 2) fingerprint matching making use of the extracted features to find the correspondence and similarity between the fingerprint images. Use of global features in minutia-based fingerprint recognition schemes enhances their recognition capability but at the expense of a substantially increased complexity. The recognition accuracies of most of the fingerprint recognition schemes, which rely on some sort of crisp clustering of the fingerprint features, are adversely affected due to the problems associated with the behavioral and anatomical characteristics of the fingerprints. The objective of this research is to develop efficient and cost-effective techniques for fingerprint recognition, that can meet the challenges arising from using both the local and global features of the fingerprints as well as effectively deal with the problems resulting from the crisp clustering of the fingerprint features. To this end, the structural information of local and global features of fingerprints are used for their decomposition, representation and matching in a multilevel hierarchical framework. The problems associated with the crisp clustering of the fingerprint features are addressed by incorporating the ideas of fuzzy logic in developing the various stages of the proposed fingerprint recognition scheme.
In the first part of this thesis, a novel low-complexity multilevel structural scheme for fingerprint recognition (MSFR) is proposed by first decomposing fingerprint images into regions based on crisp partitioning of some global features of the fingerprints. Then, multilevel feature vectors representing the structural information of the fingerprints are formulated by employing both the global and local features, and a fast multilevel matching algorithm using this representation is devised.
Inspired by the ability of fuzzy-based clustering techniques in dealing more effectively with the natural patterns, in the second part of the thesis, a new fuzzy based clustering technique that can deal with the partitioning problem of the fingerprint having the behavioral and anatomical characteristics is proposed and then used to develop a fuzzy based multilevel structural fingerprint recognition scheme. First, a histogram analysis fuzzy c-means (HA-FCM) clustering technique is devised for the partitioning of the fingerprints. The parameters of this partitioning technique, i.e., the number of clusters and the set of initial cluster centers, are determined in an automated manner by employing the histogram of the fingerprint orientation field. The development of the HA-FCM partitioning scheme is further pursued to devise an enhanced HA-FCM (EAH-FCM) algorithm. In this algorithm, the smoothness of the fingerprint partitioning is improved through a regularization of the fingerprint orientation field, and the computational complexity is reduced by decreasing the number of operations and by increasing the convergence rate of the underlying iterative process of the HA-FCM technique. Finally, a new fuzzy based fingerprint recognition scheme (FMSFR), based on the EHA-FCM partitioning scheme and the basic ideas used in the development of the MSFR scheme, is proposed.
Extensive experiments are conducted throughout this thesis using a number of challenging benchmark databases. These databases are selected from the FVC2002, FVC2004 and FVC2006 competitions containing a wide variety of challenges for fingerprint recognition. Simulation results demonstrate not only the effectiveness of the proposed techniques and schemes but also their superiority over some of the state-of-the-art techniques, in terms of the recognition accuracy and the computational complexity.
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Meghdadi, Amir Hossein. "Fuzzy Tolerance Neighborhood Approach to Image Similarity in Content-based Image Retrieval." 2012. http://hdl.handle.net/1993/8094.
Full textAbstract:
The main contribution of this thesis, is to define similarity measures between two images with the main focus on content-based image retrieval (CBIR). Each image is considered as a set of visual elements that can be described with a set of visual descriptions (features). The similarity between images is then defined as the nearness between sets of elements based on a tolerance and a fuzzy tolerance relation.
A tolerance relation is used to describe the approximate nature of the visual perception. A fuzzy tolerance relation is adopted to eliminate the need for a sharp threshold and hence model the gradual changes in perception of similarities. Three real valued similarity measures as well as a fuzzy valued similarity measure are proposed. All of the methods are then used in two CBIR experiments and the results are compared with classical measures of distance (namely, Kantorovich, Hausdorff and Mahalanobis). The results are compared with other published research papers. An important advantage of the proposed methods is shown to be their effectiveness in an unsupervised setting with no prior information. Eighteen different features (based on color, texture and edge) are used in all the experiments. A feature selection algorithm is also used to train the system in choosing a suboptimal set of visual features.