Muitos dos problemas hidrodinÃmicos que envolvem a propagaÃÃo de ondas ao longo da extensÃo em canais naturais sÃo resolvidos atravÃs da equaÃÃo de Saint-Venant. Na maioria das aplicaÃÃes prÃticas de propagaÃÃo de onda em canais naturais os termos de inÃrcia sÃo negligenciados, assim o sistema de equaÃÃo de Saint-Venant à reduzido a uma equaÃÃo parabÃlica, conhecida como equaÃÃo da onda difusiva. Esta pesquisa tem como objetivo aplicar a teoria Fuzzy nos modelos da propagaÃÃo da onda difusa em canais naturais, a fim de verificar as incertezas em relaÃÃo aos parÃmetros hidrodinÃmicos presentes nesses modelos. AtravÃs do MÃtodo das DiferenÃas Finitas ImplÃcito foram resolvidas as equaÃÃes diferenciais parciais, na sua forma fuzzy. Para a realizaÃÃo de um conjunto de simulaÃÃes para os mais diferentes cenÃrios no corpo hÃdrico foi desenvolvido um programa computacional, em linguagem FORTRAN. Os resultados mostram que o comportamento da propagaÃÃo da onda difusiva sofre forte influÃncia dos parÃmetros hidrÃulicos, declividade e nÃmero de Manning. De acordo com os resultados apresentados, concluiu-se que a aplicaÃÃo da Teoria Fuzzy, em sistemas hidrodinÃmicos, na avaliaÃÃo de incertezas à uma alternativa viÃvel para determinaÃÃo do risco de ocorrÃncia de enchentes e assim ser mais uma ferramenta de apoio em programas de GestÃo de Recursos HÃdricos. / Many of the problems which involve hydrodynamic wave propagation along the extension in natural channels are resolved through the Saint-Venant equation. In most the wave propagation channels practical applications in natural terms of inertia are neglected, so the Saint-Venant equation system is reduced to an equation satellite, known as the diffusive wave equation. This research aims apply the Fuzzy theory to model the propagation of diffused wave in natural channels in order to check the uncertainties related to the hydrodynamic parameters present in these models. Through the Finite Difference Method Implicit differential equations were solved Partial in a fuzzy way. To carry out a set of simulations for the most different scenarios in the water body was developed a computer program, in language FORTRAN. The results show that the behavior of wave propagation diffusive suffers strong influence of hydraulic parameters, slope and number of Manning. According to the presented results, it was concluded that the application of Fuzzy Theory in hydrodynamic systems, evaluation of uncertainty is a viable alternative to determining the risk of flooding and thus be more a support tool in Water Resources Management programs.

Desde su aparición a mediados de los años 60, la Teoría de Conjuntos Difusos se ha venido aplicando con éxito a la resolución de problemas en ámbitos muy diversos, que resultan difíciles de tratar con los métodos clásicos, principalmente por la presencia de incertidumbres no aleatorias en su descripción. En estos casos, el problema no tiene una solución cerrada en forma de expresión matemática, pero sí suele tenerla en forma de un conjunto de reglas expresadas en lenguaje natural y, por consiguiente, impreciso. Un ejemplo típico es el problema de conducir un automóvil.En el ámbito de la ingeniería, el núcleo de cualquier solución difusa actual es un sistema lógico difuso, encargado de obtener las salidas a partir de las entradas en un proceso de tres etapas: füzzificación, inferencia y desfuzzifícación. Hasta la fecha, la totalidad de sistemas difusos efectúan sus razonamientos basándose solamente en los valores actuales de las entradas. Ello ha dado como resultado que los sistemas de inferencia difusa sean, desde el punto de vista matemático, sistemas no lineales algebraicos. Este hecho contrasta fuertemente con el entorno en que dichos sistemas suelen emplearse. En efecto, la mayoría de aplicaciones se construyen y utilizan en entornos dinámicos, los cuales son capaces de presentar comportamientos mucho más complejos que los sistemas estáticos. Cabe entonces preguntarse si el uso de sistemas difusos dinámicos -es decir, aquellos en que sus salidas dependan no sólo de los valores presentes de las entradas sino también de los pasados-, aportaría mejoras respecto a las soluciones difusas actuales.En esta tesis se ha desarrollado una metodología para incorporar conceptos temporales difusos a los sistemas de inferencia difusa tradicionales. Para ello se ha propuesto una forma simple y eficaz de representar los citados conceptos en un entorno de ingeniería. Posteriormente se ha mostrado cómo introducirlos en las reglas difusas tradicionales, y se ha desarrollado un algoritmo para efectuar la inferencia en esta nueva situación. Se obtienen finalmente dos algoritmos distintos para dos casos diferenciados, pero ambas expresiones presentan la interesante propiedad de poder interpretarse como una convolución, tradicional en uno de los casos y una nueva forma que hemos denominado convolution difusa para el otro caso. Estas expresiones se pueden realizar por tanto de una forma muy elegante mediante circuitos analógicos o digitales.La metodología desarrollada requiere que los conceptos temporales difusos que se manejan deban realizarse mediante la respuesta impulsional de un circuito lineal. Ello remite al problema del diseño de filtros desde el punto de vista temporal, mucho menos estudiado que desde el punto de vista frecuencial. A resultas de ello se dedica una parte de la presente tesis a establecer las pautas a seguir en el proceso de diseño de dichos filtros, valiéndose de técnicas de aproximación y de optimización. Finalmente se presentan un ejemplo de aplicación, de interés tanto teórico como práctico. En él se presenta un sistema de reconocimiento simple de comandos verbales, basado en las técnicas propuestas en la presente tesis. Los resultados obtenidos han mostrado que con una estructura muy simple es posible obtener una discriminación más que suficiente entre las órdenes programadas, con la ventaja que presenta el realizar el sistema de forma totalmente analógica.

As the complexity of a spatial phenomenon increases, traditional modeling becomes impractical. Alternatively, data-driven modeling, which is based on the analysis of data characterizing the phenomena, can be used. In this thesis, the generation of understandable and reliable spatial models using observational data is addressed. An interpretability oriented data-driven fuzzy modeling approach is proposed. The methodology is based on construction of fuzzy models from data, tuning and fuzzy model simplification. Mamdani type fuzzy models with triangular membership functions are considered. Fuzzy models are constructed using fuzzy clustering algorithms and simulated annealing metaheuristic is adapted for the tuning step. To obtain compact and interpretable fuzzy models a simplification methodology is proposed. Simplification methodology reduced the number of fuzzy sets for each variable and simplified the rule base. Prototype software is developed and mean annual precipitation data of Turkey is examined as case study to assess the results of the approach in terms of both precision and interpretability. In the first step of the approach, in which fuzzy models are constructed from data, &quot / Fuzzy Clustering and Data Analysis Toolbox&quot / , which is developed for use with MATLAB, is used. For the other steps, the optimization of obtained fuzzy models from data using adapted simulated annealing algorithm step and the generation of compact and interpretable fuzzy models by simplification algorithm step, developed prototype software is used. If the accuracy is the primary objective then the proposed approach can produce more accurate solutions for training data than geographically weighted regression method. The minimum training error value produced by the proposed approach is 74.82 mm while the error obtained by geographically weighted regression method is 106.78 mm. The minimum error value on test data is 202.93 mm. An understandable fuzzy model for annual precipitation is generated only with 12 membership functions and 8 fuzzy rules. Furthermore, more interpretable fuzzy models are obtained when Gath-Geva fuzzy clustering algorithms are used during fuzzy model construction.

[Truncated abstract] Fuzzy inference systems (FIS) are information processing systems using fuzzy logic mechanism to represent the human reasoning process and to make decisions based on uncertain, imprecise environments in our daily lives. Since the introduction of fuzzy set theory, fuzzy inference systems have been widely used mainly for system modeling, industrial plant control for a variety of practical applications, and also other decisionmaking purposes; advanced data analysis in medical research, risk management in business, stock market prediction in finance, data analysis in bioinformatics, and so on. Many approaches have been proposed to address the issue of automatic generation of membership functions and rules with the corresponding subsequent adjustment of them towards more satisfactory system performance. Because one of the most important factors for building high quality of FIS is the generation of the knowledge base of it, which consists of membership functions, fuzzy rules, fuzzy logic operators and other components for fuzzy calculations. The design of FIS comes from either the experience of human experts in the corresponding field of research or input and output data observations collected from operations of systems. Therefore, it is crucial to generate high quality FIS from a highly reliable design scheme to model the desired system process best. Furthermore, due to a lack of a learning property of fuzzy systems themselves most of the suggested schemes incorporate hybridization techniques towards better performance within a fuzzy system framework. ... This systematic enhancement is required to update the FIS in order to produce flexible and robust fuzzy systems for unexpected unknown inputs from real-world environments. This thesis proposes a general framework of Adaptive T-S (Takagi-Sugeno) type Rough-Fuzzy Inference Systems (ARFIS) for a variety of practical applications in order to resolve the problems mentioned above in the context of a Rough-Fuzzy hybridization scheme. Rough set theory is employed to effectively reduce the number of attributes that pertain to input variables and obtain a minimal set of decision rules based on input and output data sets. The generated rules are examined by checking their validity to use them as T-S type fuzzy rules. Using its excellent advantages in modeling non-linear systems, the T-S type fuzzy model is chosen to perform the fuzzy inference process. A T-S type fuzzy inference system is constructed by an automatic generation of membership functions and rules by the Fuzzy C-Means (FCM) clustering algorithm and the rough set approach, respectively. The generated T-S type rough-fuzzy inference system is then adjusted by the least-squares method and a conjugate gradient descent algorithm towards better performance within a fuzzy system framework. To show the viability of the proposed framework of ARFIS, the performance of ARFIS is compared with other existing approaches in a variety of practical applications; pattern classification, face recognition, and mobile robot navigation. The results are very satisfactory and competitive, and suggest the ARFIS is a suitable new framework for fuzzy inference systems by showing a better system performance with less number of attributes and rules in each application.

Thesis (Ph. D.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Systems Science and Industrial Engineering, 2008. / Includes bibliographical references.

[pt] Esta tese propõe uma metodologia para obter uma solução para problemas de programação linear fuzzy. A metodologia aqui descrita apresenta um conjunto de soluções em que tanto os valores das variáveis quanto o valor ótimo para a função de custo, ou função objetivo, possuem uma faixa de valores possíveis. Assim, é possível fornecer um conjunto de soluções factíveis que atendam a diferentes cenários, além de fornecer ao tomador de decisões uma ferramenta de análise mais útil, permitindo que sejam analisadas outras soluções possíveis antes de se escolher uma solução em particular. O problema é resolvido de forma iterativa, tornando mais simples e de fácil aplicação a metodologia desenvolvida. / [en] This work proposes an approach to obtain a solution to linear fuzzy programming problems. The approach described here presents a solution set in where both the variables values and the cost function optimun value to have an associated membership function. Thus, it is possible to provided not only a feasible solution set applicable to different scenarios but also to supply the decision maker with a more powerful tool for the analysis of other possible solutions. The problem is solved in an interactive way, so that the developed is approach easily applicable and simple to handle

Many concrete problems, ranging from Portfolio selection to Water resource management, may be cast into a multiobjective programming framework. The simplistic way of superseding blindly conflictual goals by one objective function let no chance to the model but to churn out meaningless outcomes. Hence interest of discussing ways for tackling Multiobjective Programming Problems. More than this, in many real-life situations, uncertainty and imprecision are in the state of affairs. In this dissertation we discuss ways for solving Multiobjective Programming Problems with fixed and fuzzy coefficients. No preference, a priori, a posteriori, interactive and metaheuristic methods are discussed for the deterministic case. As far as the fuzzy case is concerned, two approaches based respectively on possibility measures and on Embedding Theorem for fuzzy numbers are described. A case study is also carried out for the sake of illustration. We end up with some concluding remarks along with lines for further development, in this field. / Operations Research / M. Sc. (Operations Research)

Made available in DSpace on 2015-03-03T15:47:46Z (GMT). No. of bitstreams: 1 AndersonPC_DISSERT.pdf: 1402040 bytes, checksum: 960b15bc1392a94fb7ba8ba980e3a0b4 (MD5) Previous issue date: 2012-12-20 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Atualmente, h? diferentes defini??es de implica??es fuzzy aceitas na literatura. Do ponto de vista te?rico, esta falta de consenso demonstra que h? discord?ncias sobre o real significado de "implica??o l?gica" nos contextos Booleano e fuzzy. Do ponto de vista pr?tico, isso gera d?vidas a respeito de quais "operadores de implica??o" os engenheiros de software devem considerar para implementar um Sistema Baseado em Regras Fuzzy (SBRF). Uma escolha ruim destes operadores pode implicar em SBRF's com menor acur?cia e menos apropriados aos seus dom?nios de aplica??o. Uma forma de contornar esta situa??o e conhecer melhor os conectivos l?gicos fuzzy. Para isso se faz necess?rio saber quais propriedades tais conectivos podem satisfazer. Portanto, a m de corroborar com o significado de implica??o fuzzy e corroborar com a implementa??o de SBRF's mais apropriados, v?rias leis Booleanas t?m sido generalizadas e estudadas como equa??es ou inequa??es nas l?gicas fuzzy. Tais generaliza??es s?o chamadas de leis Boolean-like e elas n?o s?o comumente v?lidas em qualquer sem?ntica fuzzy. Neste cen?rio, esta disserta??o apresenta uma investiga??o sobre as condi??es suficientes e necess?rias nas quais tr?s leis Booleanlike ?like ? y ? I(x, y), I(x, I(y, x)) = 1 e I(x, I(y, z)) = I(I(x, y), I(x, z)) ?? se mant?m v?lidas no contexto fuzzy, considerando seis classes de implica??es fuzzy e implica??es geradas por automorfismos. Al?m disso, ainda no intuito de implementar SBRF's mais apropriados, propomos uma extens?o para os mesmos / There are more than one acceptable fuzzy implication definitions in the current literature dealing with this subject. From a theoretical point of view, this fact demonstrates a lack of consensus regarding logical implication meanings in Boolean and fuzzy contexts. From a practical point of view, this raises questions about the implication operators" that software engineers must consider to implement a Fuzzy Rule Based System (FRBS). A poor choice of these operators generates less appropriate FRBSs with respect to1 their application domain. In order to have a better understanding of logical connectives, it is necessary to know the properties that they can satisfy. Therefore, aiming to corroborate with fuzzy implication meaning and contribute to implementing more appropriate FRBSs to their domain, several Boolean laws have been generalized and studied as equations or inequations in fuzzy logics. Those generalizations are called Booleanlike laws and a lot of them do not remain valid in any fuzzy semantics. Within this context, this dissertation presents the investigation of sucient and necessary conditions under which three Boolean-like laws | y I(x; y), I(x; I(y; x)) = 1 and I(x; I(y; z)) = I(I(x; y); I(x; z)) | hold for six known classes of fuzzy implications and for implications generated by automorphisms. Moreover, an extension to FRBSs is proposed

Submitted by Adriely Bruce (adriely_bruce@hotmail.com) on 2016-12-16T13:13:00Z No. of bitstreams: 1 Dissertação - Diego Câmara Sales.pdf: 3202633 bytes, checksum: b132b1fefafdda85faf38c42f7092ae2 (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2016-12-22T15:15:35Z (GMT) No. of bitstreams: 1 Dissertação - Diego Câmara Sales.pdf: 3202633 bytes, checksum: b132b1fefafdda85faf38c42f7092ae2 (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2016-12-22T15:16:44Z (GMT) No. of bitstreams: 1 Dissertação - Diego Câmara Sales.pdf: 3202633 bytes, checksum: b132b1fefafdda85faf38c42f7092ae2 (MD5) / Made available in DSpace on 2016-12-22T15:16:44Z (GMT). No. of bitstreams: 1 Dissertação - Diego Câmara Sales.pdf: 3202633 bytes, checksum: b132b1fefafdda85faf38c42f7092ae2 (MD5) Previous issue date: 2014-07-26 / FAPEAM - Fundação de Amparo à Pesquisa do Estado do Amazonas / Nowadays kind quadrotor helicopters are becoming popular in developing research in unmanned aerial vehicles UAV, for its ability to planar and vertical landing in hard to reach places. The control of most of these vehicles is based on physical or experimental dynamic models, and may have a high cost due to the time of project development. In this work is presented the stability control using fuzzy logic, which eliminates the need for a dynamic model for the controller design. Using the least amount of linguistic rules in order to enable the implementation of the controller in embedded systems with low computational capacity. / Hoje em dia os helicópteros do tipo quadrotor estão se tornando populares no desenvolvimento de pesquisas em veículos aéreos não tripulados VANT, pela sua capacidade de pouso vertical e de planar em locais de difícil acesso. O controle da maioria destes veículos é baseado em modelos dinâmicos físicos ou experimentais, podendo apresentar um alto custo devido ao tempo de desenvolvimento do projeto. Neste trabalho é apresentado o controle de estabilidade de um quadrotor utilizando lógica fuzzy, que dispensa a necessidade de um modelo dinâmico para o projeto do controlador. Utilizando a menor quantidade de regras linguísticas com o intuito de viabilizar a implementação do controlador em sistemas embarcados de baixa capacidade computacional simplificando o desenvolvimento do controlador de estabilidade do sistema proposto.

This diploma thesis deals with the evaluation of security companies for ZETOR TRACTORS a.s. using fuzzy logic models. The main part of the thesis consists of proposals for the evaluation of the suppliers' evaluation of the company. Decision models are created in Microsoft Excel and Matlab. Another part of the thesis deals with analysis and comparison of results from both programs.