Orientador: João de Deus Mendes da Silva / Dissertação (mestrado profissional) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica / Made available in DSpace on 2018-08-15T01:57:01Z (GMT). No. of bitstreams: 1 Barros_AntonioMagno_M.pdf: 1363614 bytes, checksum: d02b8dd0f8f8eafcf19aab637ef36f26 (MD5) Previous issue date: 2009 / Resumo: Os fenômenos epidemiológicos apresentam vários tipos de subjetividades, nas quais, em muitas ocasiões, são tratadas de maneira eficiente pelos modelos clássicos. Entretanto, a lógica fuzzy se apresenta de maneira adequada para tratar tais subjetividades. Neste trabalho, realizamos um estudo sobre os modelos epidemiológicos do tipo SI, SIS e SIR. Em seguida apresentamos os principais conceitos da teoria dos conjuntos fuzzy, controladores fuzzy e sistemas dinâmicos p-fuzzy. Fazemos, também um estudo dos modelos epidemiológicos fuzzy onde utilizamos o valor esperado fuzzy como defuzificador. Por fim, propomos uma comparação entre os modelos clássicos, p-fuzzy e valor esperado fuzzy. / Abstract: The epidemiological phenomena have several types of subjectivities, in which, on many occasions, are handled efficiently by classical models. However, fuzzy logic is presented properly to treat such subjectivities. We carried out a study on the epidemiological models of type SI, SIS and SIR. The following are the main concepts of the theory of fuzzy sets, fuzzy controllers and p-fuzzy dynamic systems. We are also a study of epidemiological models where we use the fuzzy expected value as fuzzy defuzificador. Finally, we propose a comparison between the classical models, p-fuzzy and fuzzy expected value. / Mestrado / Biomatematica / Mestre em Matemática

Orientador: Rodney Carlos Bassanezi / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica / Made available in DSpace on 2018-08-12T05:14:00Z (GMT). No. of bitstreams: 1 Santos_LuizRafaeldos_M.pdf: 1659435 bytes, checksum: 5f919360eaa92970fd9189ca97ecdb83 (MD5) Previous issue date: 2008 / Resumo: O objetivo deste trabalho é propor um modelo utilizando Sistemas Baseados em Regras Fuzzy (SBRF) que possa controlar, do ponto de vista teórico, uma determinada espécie, considerada como praga em uma lavoura ou plantação. Faz-se primeiro uma introdução à Teoria dos Conjuntos Fuzzy, bem como aos sistemas especialistas fuzzy que utilizam regras de inferência, através do método de inferência de Mamdani e que utilizem o método de defuzzificação por centro de massa. Logo após, são abordados os conceitos de Sistemas Dinâmicos P-Fuzzy uni e bidimensionais desenvolvidos por Silva [24] e Cecconelo [7]. É proposto um novo tipo de Sistema Dinâmico P-fuzzy para modelar a dinâmica populacional, de uma espécie ou de um sistema presa-predador, que leve em conta fatores extrínsecos da(s) espécie(s) envolvida(s). Estes fatores são representados por uma Condição Ambiental, que é definida e acoplada aos Sistemas P-fuzzy usuais. Ainda, usando um controlador fuzzy propomos um sistema de controle híbrido com controladores biológicos e químicos (biocidas) em que as espécies que interagem estão sujeitas também às condições ambientais. Simulações e experimentos computacionais feitos com o auxílio da Fuzzy Logic Toolbox do software Matlab são realizados e seus resultados são comentados. / Abstract: The aim of this work is to propose determined model, using Fuzzy Rule-based Systems (FRBS), wich can control, in a theoretical manner, a species considered a plage in a farming or plantation. An introduction to Fuzzy Sets is maded as well as maded to Fuzzy Systems wich uses inference mIes, through the Mamdani method of inference and that uses Centroid as defuzzification method. After this, the concepts of uni and bidimensional P-fuzzy Dynamic Systems are studied and approached, as developed by Silva [24] and Cecconelo [7]. We propose a new type of P-fuzzy Dynamic System, to model the population dynamics of one species system or of a prey-predator system, that takes into account extrinsic factors of the species involved. Such factores are represented by an Enviromental Condition wich is defined and connected to the usual P-fuzzy Systems. Finnaly, using a Fuzzy Controller, we propose a system for hybrid control with biological and chemical (biocides) controllers in wich the density of species are also influenced by enviromental conditions. Computational simulations and experiments are made with the aid of Matlab Fuzzy Logic Toolbox and its results are commented. / Mestrado / Biomatematica / Mestre em Matemática Aplicada

M.Ing. / Adaptive traction control can greatly enhance the mobility of vehicles on varying road surfaces. Traction control includes Antilock Braking Systems (ABS) and Antislip Regulation Systems (ASR). During braking, wheel slip is controlled with ABS, while wheel slip during acceleration is controlled by an ASR. Since the friction between a vehicle's tyre and the road surface is a function of wheel slip, there is an optimum wheel slip value at which the best road holding performance can be achieved. This optimum wheel slip value will however change with differing road surfaces and vehicle speeds. Optimising the wheel slip values has several advantages: both vehicle stopping and acceleration distances can be optimised, vehicle handling during in-turn braking and acceleration are optimised and tyre wear is reduced. Currently there are various ABS and ASR systems available, with the common goal of optimising wheel slip. These systems are however mechanically complex, while the operation of both these systems is usually triggered when some wheel slip value is exceeded. Differing road surfaces can greatly influence the effectiveness of these systems. The central theme of this research is the development of a fuzzy logic control algorithm for vehicle traction control. The control algorithm is tested with an experimental setup. The operating conditions experienced by an ABS are closely simulated in order to study the feasibility of fuzzy logic for traction control. The results obtained indicates the viability of fuzzy logic for wheel slip control. Confirmation of these results, obtained with the experimental ABS, have to be validated during vehicle testing. The main goal is to improve the performances of existing traction control systems and the feasibility of fuzzy controllers in automobile applications.

M.Ing. / The principal reasons for the development of a prototype improved control system are the high maintenance costs and unreliability of Spoomet's fleet of class 6E/6E1 resistor technology electric main line locomotives. These factors may largely be attributed to two fundamental shortcomings of the existing locomotive control systems, namely the lack of inherent feedback and application of inconsequent control practices during acceleration from standstill. The improved control system features the application of a rule based fuzzy controller, implementing human skill and experience to control tractive effort of a resistor technology main line locomotive. The aim of the fuzzy controller is to accelerate the train from standstill to approximately 35 km/h, smoothly and safely. The prototype fuzzy controller was implemented with a personal computer using an advanced fuzzy logic development system. A simulation model was developed for the locomotive and the load. This model was used to first test the structure of the controller and the initial rule blocks. Following the verification of the fuzzy rules on the simulation model, a relay interface was developed to implement the operation of the control system in coupled mode with the existing control system on a locomotive. The interactive fine-tuning and evaluation of the fuzzy rules were performed during this phase of the development. The test results include the successful evaluation of the prototype fuzzy controller under a variety of typical and "worst case" operating conditions, as well as under conditions of wheel slip. The industrialisation and long term considerations for continued development of the fuzzy logic controller are described in the conclusion.

M.Ing. / Fuzzy controllers are increasingly being accepted by engineers and scientists alike as a viable alternative for classical controllers. The processes involved in fuzzy controllers closely imitate human control processes. Human responses to stimuli are not governed by transfer functions and neither are those from fuzzy controllers. The fuzzy approach is of course not the answer to all problems, but it can clearly be very successful, and can also be helpful to anyone involved in developing control systems. This study however is devoted to the environmental control task within greenhouses and the fuzzy approach is proposed in order to fulfil this task. To create near optimal conditions within a greenhouse for plant growth two environmental factors are proposed to be controlled namely the temperature and relative humidity. These factors are interdependent and they make the environmental control within a greenhouse a multi-variable control problem. Furthermore, the non-linear physical phenomena governing the dynamics of temperature and relative humidity in such a process makes it very difficult to model and to control using traditional techniques. Thus, it can be said that the environmental control in greenhouses is an art, that only expert growers bring to near perfection. The central theme of this study is the development of a multi-input-single-output heuristic rule-based fuzzy logic control algorithm, for environmental control within a greenhouse. This study is intended to improve existing environmental control systems by implementing this control technique. The control algorithm is tested in an experimental greenhouse and the results obtained indicate that fuzzy logic control is viable for environmental control within greenhouses.

The broad goal in this thesis is to enumerate elements and fuzzy subsets of a finite set enjoying some useful properties through the well-known counting technique of the principle of inclusion-exclusion. We consider the set of membership values to be finite and uniformly spaced in the real unit interval. Further we define an equivalence relation with regards to the cardinalities of fuzzy subsets providing the Möbius function and Möbius inversion in that context.

In this study we apply the knowledge of fuzzy sets to group structures and also to decision-making implications. We study fuzzy subgroups of finite abelian groups. We set G = Z[subscript p[superscript n]] + Z[subscript q[superscript m]]. The classification of fuzzy subgroups of G using equivalence classes is introduced. First, we present equivalence relations on fuzzy subsets of X, and then extend it to the study of equivalence relations of fuzzy subgroups of a group G. This is then followed by the notion of flags and keychains projected as tools for enumerating fuzzy subgroups of G. In addition to this, we use linear ordering of the lattice of subgroups to characterize the maximal chains of G. Then we narrow the gap between group theory and decision-making using relations. Finally, a theory of the decision-making process in a fuzzy environment leads to a fuzzy version of capital budgeting. We define the goal, constraints and decision and show how they conflict with each other using membership function implications. We establish sets of intervals for projecting decision boundaries in general. We use the knowledge of triangular fuzzy numbers which are restricted field of fuzzy logic to evaluate investment projections.

FSW is a welding technique invented and patented by The Welding Institute in 1991. This welding technique utilises the benefits of solid-state welding to materials regarded as difficult to weld by fusion processes. The productivity of the process was not optimised as the real-time dynamics of the material and tool changes were not considered. Furthermore, the process has a plastic weld region where no traditional modelling describing the interaction between the tool and work piece is available. Fuzzy logic technology is one of the artificial intelligent strategies used to improve the control of the dynamics of industrial processes. Fuzzy control was proposed as a viable solution to improve the productivity of the FSW process. The simulations indicated that FLC can use feed rate and welding speed to adaptively regulate the feed force and tool temperature respectively, irrespective of varying tool and material change. The simulations presented fuzzy logic technology to be robust enough to regulate FSW process in the absence of accurate mathematical models.

Orientador: Ailton Akira Shinoda / Coorientador: Ruy de Oliveira / Banca: Christiane Marie Schweitzer / Banca: Maria Lúcia Martins Lopes / Banca: Anderson Castro Soares de Oliveira / Banca: Carlos Dias Maciel / Resumo: Nos últimos anos têm-se percebido um forte crescimento no uso da tecnologia sem fio 802.11 (Wireless Local Area Network - WLAN) e os mecanismos de segurança implementados pelas emendas IEEE 802.11i e IEEE 802.11w têm se mostrado pouco eficazes no combate a ataques contra a disponibilidade dos serviços da WLAN. Os sistemas detectores de intrusão surgem como uma forma de auxiliar as redes de computadores neste combate contra a indisponibilização dos serviços. Nesta tese é proposto um modelo de detecção de intrusos chamado Kappa-PSO-ARTMAP Fuzzy, onde primeiramente a base de dados original é pré-processada, por meio de uma técnica de seleção de atributos baseada em rede neural ARTMAP Fuzzy e coeficiente Kappa, para reduzir a quantidade de atributos, deixando apenas as características mais representativas. A seguir, aplica-se a técnica de otimização por enxame de partículas (particle optimization swarm - PSO) na seleção de um conjunto de critérios (parâmetro de escolha, parâmetro de vigilância do módulo ARTa, taxa de treinamento e acréscimo do parâmetro de vigilância do módulo ARTa) empregados no treinamento do classificador de ataques, de forma a maximizar a identificação correta de amostras classificadas. O algoritmo de detecção de intrusos empregado no classificador de ataques é a rede neural ARTMAP Fuzzy. O desempenho desta nova estratégia é avaliado sobre três bases de dados coletadas respectivamente de uma rede simulada cabeada, uma rede infraestruturada sem fio com criptografia WEP (Wired Equivalent Privacy) e WPA (WiFi Protected Access) habilitadas e uma rede infraestruturada sem fio com criptografia WPA2 (WiFi Protected Access version 2) habilitada. Os resultados obtidos na avaliação da metodologia Kappa-PSO-ARTMAP Fuzzy demonstram a diminuição... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: In the last years have seen a strong increase in the 802.11 wireless local area network (WLAN) technologies use, and the security mechanisms implemented by amendments IEEE 802.11i and IEEE 802.11w have proven not very effective in combating attacks against availability of WLAN services. Intrusion detection systems emerge as a way to help computer networks in this combat against the deny of services. In this thesis it's proposed a model of intrusion detection called Kappa-PSO-Fuzzy ARTMAP, where initially the original database is pre-processed through a feature selection technique based on ARTMAP Fuzzy neural network and Kappa coefficient for reduce the amount of attributes, leaving only the most representative features. Then, apply the particle swarm optimization (PSO) technique in searching a set of criteria (choice parameter, ARTa module vigilance parameter, training rate and increase in the ARTa module vigilance paramater) employees in training attacks classifier, in order to maximize the accurate identification of classified samples. The intrusion detection algorithm used in the attacks classifier is the ARTMAP Fuzzy neural network. The performance of this new strategy is evaluated over three colleted databases respectively in a simulated wired network, infrastructured wireless network with WEP (Wired Equivalent Privacy) and WPA (WiFi Protected Access) encryption enabled and infrastructured wireless network with WPA2 (WiFi Protected Access version 2) encryption enabled. The obtained results in the Kappa-PSO-ARTMAP Fuzzy methodology demonstrate the IDS computational cost reduction without causing... (Complete abstract click electronic access below) / Doutor

Microgrid is an essential part of the nation’s smart grid deployment plan, recognized especially for improving efficiency, reliability, flexibility, and resiliency of the electricity system. Since microgrid consists of different distributed generation units, microgrid scheduling and real-time dispatch play a crucial role in maintaining economic, reliable, and resilient operation. The control and optimization performances of the existing online approaches degrade significantly in microgrid applications with missing forecast information, large state space, and multiple probabilistic events. This dissertation focuses on these challenges and proposes efficient online learning and optimization-based approaches. For addressing the missing forecast challenges on online microgrid operations, a new fitted rolling horizon control (fitted-RHC) approach is proposed in Chapter 2. The proposed fitted-RHC approach is designed with a regression algorithm that utilizes the empirical knowledge obtain from the day-ahead forecast to make microgrid real-time decisions whenever the intra-day forecast data is unavailable. Simulation results show that the proposed fitted-RHC approach can achieve the optimal policy for the deterministic case study and perform efficiently with the uncertain environment in the stochastic case study. / Includes bibliography. / Dissertation (PhD)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection