Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Dept. of Systems Science and Industrial Engineering, 2006. / Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 1997. / Vita. Includes bibliographical references (leaves [80]-84).

Zugl.: Regensburg, Universiẗat, Diss., 2008.

Thesis (M.S.)--West Virginia University, 2009. / Title from document title page. Document formatted into pages; contains xiii, 120 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 89-93).

Universiẗat, Diss., 2004--Bonn.

No description available.

Submitted by FERNANDA ALMEIDA MARCHINI GAYER null (fer_marchini@yahoo.com.br) on 2017-12-12T16:34:52Z No. of bitstreams: 1 dissertacao.pdf: 8726620 bytes, checksum: 66b744fe24afaf15e89574cfd8c23af1 (MD5) / Approved for entry into archive by Adriana Aparecida Puerta null (dripuerta@rc.unesp.br) on 2017-12-12T18:20:47Z (GMT) No. of bitstreams: 1 gayer_fam_me_rcla.pdf: 8697307 bytes, checksum: ef684be2abf1d5c9b37f2d77c9ab5ce8 (MD5) / Made available in DSpace on 2017-12-12T18:20:47Z (GMT). No. of bitstreams: 1 gayer_fam_me_rcla.pdf: 8697307 bytes, checksum: ef684be2abf1d5c9b37f2d77c9ab5ce8 (MD5) Previous issue date: 2017-12-01 / Neste trabalho, apresentamos um estudo introdutório à Teoria de Conjuntos Fuzzy e Lógica Fuzzy, mostrando o seu potencial de aplicação através da análise de um problema numa indústria química e da introdução de alguns conceitos dessa teoria a alunos do Ensino Médio. Quanto ao problema da indústria química, o objetivo é assessorar uma equipe de gestão de manutenção de uma certa indústria química na tomada de decisão quanto ao momento oportuno para realização de intervenção em uma bomba industrial. Esta equipe observou como dificuldade em sua gestão de custos a manutenção preventiva de uma certa bomba de seu processo. Para isso, dados dos processos são analisados e modelados matematicamente utilizando a lógica fuzzy, produzindo um resultado que direciona corretamente os profissionais no momento da tomada de decisão, sobrepondo a manutenção preventiva existente pela manutenção preditiva, baseada em dados do processo. Foi criado um sistema computacional que favorece este processo de forma automática e simplificada, evitando burocracias legais quanto à questão de licenciamento de softwares do mercado, de forma a realizar os cálculos, além de facilitar a incorporação de dados e análises de forma intuitiva, não necessitando de maiores treinamentos para tanto. Por fim, durante o estudo da lógica fuzzy e pesquisas relacionadas, foi detectada a possibilidade de aplicação prática para estudantes do Ensino Médio. Dessa forma, uma aula expositiva com atividades originais foi realizada para apresentar os conjuntos e a lógica fuzzy, mostrando a capacidade dos alunos do Ensino Médio em assimilar os conteúdos já citados. / In this work, we present an introductory study to Fuzzy Set Theory and Fuzzy Logic, showing its potential application through the analysis of a problem in a chemical industry and the introduction of some concepts of this theory to high school students. As for the problem of the chemical industry, the objective is to advise a manufacturing management team of a certain chemical industry in the decision making for an opportune time for an intervention in an industrial pump. This team observed as a difficulty in its cost management a preventive maintenance of a certain pump of its process. For this, the data of the processes are analysed and modelled mathematically using a fuzzy logic, producing a result that correctly directs the professionals at the moment of the decision making, overlapping the existing preventive maintenance, by the base predictive maintenance in process data. It was created a computer system that helps this process in an automatic and simplified way, avoiding legal bureaucracies regarding the licensing of software in the market, in order to perform the calculations, besides facilitating the incorporation of data and analyses in an intuitive way, not requiring of greater training for both. Finally, during the study of fuzzy logic and related research, it was detected as a possibility of practical application for high school students. Thus, an expository class with original activities was performed to present the sets and a fuzzy logic, showing the ability of the high school students to assimilate the referred contents.

[pt] Esta dissertação investiga a utilização de sistemas Neuro- Fuzzy Hierárquicos BSP (Binary Space Partitioning) para aplicações em classificação de padrões, previsão, sistemas de controle e extração de regras fuzzy. O objetivo é criar um modelo Neuro-Fuzzy Hierárquico BSP do tipo Mamdani a partir do modelo Neuro-Fuzzy Hierárquico BSP Class (NFHB-Class) que é capaz de criar a sua própria estrutura automaticamente e extrair conhecimento de uma base de dados através de regras fuzzy, lingüisticamente interpretáveis, que explicam a estrutura dos dados. Esta dissertação consiste de quatros etapas principais: estudo dos principais sistemas hierárquicos; análise do sistema Neuro-Fuzzy Hierárquico BSP Class, definição e implementação do modelo NFHB-Mamdani e estudo de casos. No estudo dos principais sistemas hierárquicos é efetuado um levantamento bibliográfico na área. São investigados, também, os principais modelos neuro-fuzzy utilizados em sistemas de controle - Falcon e o Nefcon. Na análise do sistema NFHB- Class, é verificado o aprendizado da estrutura, o particionamento recursivo, a possibilidade de se ter um maior número de entrada - em comparação com outros sistemas neuro-fuzzy - e regras fuzzy recursivas. O sistema NFHB- Class é um modelo desenvolvido especificamente para classificação de padrões, como possui várias saídas, não é possível utilizá-lo em aplicações em controle e em previsão. Para suprir esta deficiência, é criado um novo modelo que contém uma única saída. Na terceira etapa é definido um novo modelo Neuro-Fuzzy Hierárquico BSP com conseqüentes fuzzy (NFHB-Mamdani), cuja implementação utiliza a arquitetura do NFHBClass para a fase do aprendizado, teste e validação, porém, com os conseqüentes diferentes, modificando a estratégia de definição dos conseqüentes das regras. Além de sua utilização em classificação de padrões, previsão e controle, o sistema NFHB-Mamdani é capaz de extrair conhecimento de uma base de dados em forma de regras do tipo SE ENTÃO. No estudo de casos são utilizadas duas bases de dados típicas para aplicações em classificação: Wine e o Iris. Para previsão são utilizadas séries de cargas elétricas de seis companhias brasileiras diferentes: Copel, Cemig, Light, Cerj, Eletropaulo e Furnas. Finalmente, para testar o desempenho do sistema em controle faz-se uso de uma planta de terceira ordem como processo a controlar. Os resultados obtidos para classificação, na maioria dos casos, são superiores aos melhores resultados encontrados pelos outros modelos e algoritmos aos quais foram comparados. Para previsão de cargas elétricas, os resultados obtidos estão sempre entre os melhores resultados fornecidos por outros modelos aos quais formam comparados. Quanto à aplicação em controle, o modelo NFHB-Mamdani consegue controlar, de forma satisfatória, o processo utilizado para teste. / [en] This paper investigates the use of Binary Space Partitioning (BSP) Hierarchical Neuro-Fuzzy Systems for applications in pattern classification, forecast, control systems and obtaining of fuzzy rules. The goal is to create a BSP Hierarchical Neuro-Fuzzy Model of the Mamdani type from the BSP Hierarchical Neuro-Fuzzy Class (NFHB-Class) which is able to create its own structure automatically and obtain knowledge from a data base through fuzzy rule, interpreted linguistically, that explain the data structure. This paper is made up of four main parts: study of the main Hierarchical Systems; analysis of the BSP Hierarchical Neuro-Fuzzy Class System, definition and implementation of the NFHB-Mamdani model, and case studies. A bibliographical survey is made in the study of the main Hierarchical Systems. The main Neuro-Fuzzy Models used in control systems - Falcon and Nefcon -are also investigated. In the NFHB-Class System, the learning of the structure is verified, as well as, the recursive partitioning, the possibility of having a greater number of inputs in comparison to other Neuro-Fuzzy systems and recursive fuzzy rules. The NFHB-Class System is a model developed specifically for pattern classification, since it has various outputs, it is not possible to use it in control application and forecast. To make up for this deficiency, a new unique output model is developed. In the third part, a new BSP Hierarchical Neuro-Fuzzy model is defined with fuzzy consequents (NFHB-Mamdani), whose implementation uses the NFHB-Class architecture for the learning, test, and validation phase, yet with the different consequents, modifying the definition strategy of the consequents of the rules. Aside from its use in pattern classification, forecast, and control, the NFHB-Mamdani system is capable of obtaining knowledge from a data base in the form of rules of the type IF THEN. Two typical data base for application in classification are used in the case studies: Wine and Iris. Electric charge series of six different Brazilian companies are used for forecasting: Copel, Cemig, Light, Cerj, Eletropaulo and Furnas. Finally, to test the performance of the system in control, a third order plant is used as a process to be controlled. The obtained results for classification, in most cases, are better than the best results found by other models and algorithms to which they were compared. For forecast of electric charges, the obtained results are always among the best supplied by other models to which they were compared. Concerning its application in control, the NFHB-Mamdani model is able to control, reasonably, the process used for test.

System identification is the task of constructing representative models of processes and has become an invaluable tool in many different areas of science and engineering. Due to the inherent complexity of many real world systems the application of traditional techniques is limited. In such instances more sophisticated (so called intelligent) modelling approaches are required. Neurofuzzy modelling is one such technique, which by integrating the attributes of fuzzy systems and neural networks is ideally suited to system identification. This attractive paradigm combines the well established learning techniques of a particular form of neural network i.e. generalised linear models with the transparent knowledge representation of fuzzy systems, thus producing models which possess the ability to learn from real world observations and whose behaviour can be described naturally as a series of linguistic humanly understandable rules. Unfortunately, the application of these systems is limited to low dimensional problems for which good quality expert knowledge and data are available. The work described in this thesis addresses this fundamental problem with neurofuzzy modelling, as a result algorithms which are less sensitive to the quality of the a priori knowledge and empirical data are developed. The true modelling capabilities of any strategy is heavily reliant on the model's structure, and hence an important (arguably the most important) task is structure identification. Also, due to the curse of dimensionality, in high dimensional problems the size of conventional neurofuzzy models gets prohibitively large. These issues are tackled by the development of automatic neurofuzzy model identification algorithms, which exploit the available expert knowledge and empirical data. To alleviate problems associated with the curse of dimensionality, aid model generalisation and enhance model transparency, parsimonious models are identified. This is achieved by the application of additive and multiplicative neurofuzzy models which exploit structural redundancies found in conventional systems. The developed construction algorithms successfully identify parsimonious models, but as a result of noisy and poorly distributed empirical data, these models can still generalise inadequately. This problem is addressed by the application of Bayesian inferencing techniques; a form of regularisation. Smooth model outputs are assumed and superfluous model parameters are controlled, sufficiently aiding model generalisation and transparency, and data interpolation and extrapolation. By exploiting the structural decomposition of the identified neurofuzzy models, an efficient local method of regularisation is developed. All the methods introduced in this thesis are illustrated on many different examples, including simulated time series, complex functional equations, and multi-dimensional dynamical systems. For many of these problems conventional neurofuzzy modelling is unsuitable, and the developed techniques have extended the range of problems to which neurofuzzy modelling can successfully be applied.

Conventional control systems express control solutions by means of expressions, usually mathematically based. In order to completely express the control solution, a vast amount of data is required. In contrast, knowledge-based solutions require far less plant data and mathematical expression. This reduces development time proportionally. In addition, because this type of processing does not require involved calculations, processing speed is increased, since rule process is separate and all processes can be performed simultaneously. These results in improved product quality, better plant efficiency, simplified process, etc. Within this project, conventional PID control has already been implemented, with the control parameter adjustment and loop tuning being problematic. This is mainly due to a number of external parameters that affects the stability of the process. In maintaining a consistent temperature, for example, the steam flow rate varies, the hot well temperature varies, the ambient may temperature vary. Another contributing factor, the time delay, also affects the optimization of the system, due to the fact that temperature measurement is based on principle of absorption. The normal practice in industry to avoid an unstable control condition is to have an experienced operator to switch the controller to manual, and make adjustments. After obtaining the desired PV, the controller is switched back to automatic. This research project focuses on eliminating this time loss, by implementing a knowledge-based controller, for intelligent decision-making. A FLC design tool, which allows full interaction, whilst designing the control algorithm, was used to optimize the control system. The design tool executed on a PC is connected to a PLC, which in turn is successfully integrated into the process plant.