Learning Fuzzy Rules
Mostrando 1-12 de 14 artigos, teses e dissertações.
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1. Geração de Regras de Decisão Fuzzy Utilizando a Teoria dos Rough Sets
Este trabalho propõe um novo método para gerar automaticamente regras fuzzy baseado na teoria dos rough sets e na teoria dos conjuntos fuzzy. As regras derivadas são concisas em relação ao número de termos antecedentes e apresentam alta taxa de cobertura. O sistema de classicação baseado nestas regras fuzzy foi adaptado para discriminar entre a possi
Publicado em: 2009
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2. Optimal control of a CSTR process
Designing an effective criterion and learning algorithm for find the best structure is a major problem in the control design process. In this paper, the fuzzy optimal control methodology is applied to the design of the feedback loops of an Exothermic Continuous Stirred Tank Reactor system. The objective of design process is to find an optimal structure/gains
Brazilian Journal of Chemical Engineering. Publicado em: 2008-12
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3. Abordagem neurofuzzy para modelagem de sistemas dinamicos não lineares / Neurofuzzy approach for nonlinear dynamical systems modeling
This work suggests a systematic procedure to develop models of complex nonlinear dynamical systems using neural fuzzy networks. The neural fuzzy networks are able to extract knowledge from input/output data and to encode it explicitly in the form of if-then rules. Therefore, linguistic models are obtained in a form suitable for human understanding. Two new c
Publicado em: 2008
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4. Rede Neural Difusa com T-normas Diferenciáveis e Interativas
Fuzzy sets are used in the representation of vague and imprecise knowledge. Neural networks, besides their computational parallelism, also have learning capabilities. The combination of such both paradigms is an attempt to congregate their benefits in an integrated system, such a fuzzy neural network. T-norms are functions that actuate like intersection and
Publicado em: 2007
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5. MODELOS NEURO-FUZZY HIERÁRQUICOS BSP DO TIPO 2 / TYPE-2 HIERARCHICAL NEURO-FUZZY BSP MODEL
The objective of this thesis is to create a new type-2 fuzzy inference system for the treatment of uncertainties with automatic learning and that provides an interval of confidence for its defuzzified output through the calculation of corresponding type-reduced sets. In order to attain this objective, this new model combines the paradigms of the modelling of
Publicado em: 2007
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6. Analises de series temporais e modelagem baseada em regras nebulosas / Time series analysis and modeling based on fuzzy rules the school of eletrical and computer engineering
Este trabalho propõe uma metodologia baseada em regras nebulosas para a modelagem e previsão de séries temporais. Inicialmente, os dados são pré-processados para, a seguir, ocorrer a seleção de variáveis que serão utilizadas pelos modelos de série temporal. Para essa finalidade, nesta tese propõe-se um conjunto de aproximações necessárias para
Publicado em: 2007
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7. FUZZY RULES EXTRACTION FROM SUPPORT VECTOR MACHINES (SVM) FOR MULTI-CLASS CLASSIFICATION / EXTRAÇÃO DE REGRAS FUZZY PARA MÁQUINAS DE VETOR SUPORTE (SVM) PARA CLASSIFICAÇÃO EM MÚLTIPLAS CLASSES
This text proposes a new method for fuzzy rule extraction from support vector machines (SVMs) trained to solve classification problems. SVMs are learning systems based on statistical learning theory and present good ability of generalization in real data base sets. These systems have been successfully applied to a wide variety of application. However SVMs, a
Publicado em: 2006
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8. Exploração de relações entre técnicas simbólicas e conexionistas da inteligência computacional. / Relations exploration between symbolic and connectionist techniques of computacional intelligence.
This work consists of a contribution to the area of Computational Intelligence, relating to some of its main techniques: Fuzzy Computing and Neural Computing. These techniques are being used to solve problems that are too complex for traditional algorithmic approach or mathematical modeling. However, these problems are solved easly with the apparatus that co
Publicado em: 2006
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9. HIBRID NEURO-FUZZY-GENETIC SYSTEM FOR AUTOMATIC DATA MINING / SISTEMA HÍBRIDO NEURO-FUZZY-GENÉTICO PARA MINERAÇÃO AUTOMÁTICA DE DADOS
This dissertation presents the proposal and the development of a totally automatic data mining system. The main objective is to create a system that is capable of extracting obscure information from complex databases, without demanding the presence of a technical specialist to configure it. The Hierarchical Neuro-Fuzzy Binary Space Partitioning model (NFHB)
Publicado em: 2004
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10. HIERARQUICAL NEURO-FUZZY MODELS BASED ON REINFORCEMENT LEARNING FOR INTELLIGENT AGENTS / NOVOS MODELOS NEURO-FUZZY HIERÁRQUICOS COM APRENDIZADO POR REFORÇO PARA AGENTES INTELIGENTES
This thesis investigates neuro-fuzzy hybrid models for automatic learning of actions taken by agents. The objective of these models is to provide an agent with intelligence, making it capable of acquiring and retaining knowledge and of reasoning (infer an action) by interacting with its environment. Learning in these models is performed by a non-supervised p
Publicado em: 2003
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11. HIERARCHICAL NEURO-FUZZY BSP-MAMDANI MODEL / MODELO NEURO-FUZZY HIERÁRQUICOS BSP MAMDANI
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 (NFH
Publicado em: 2002
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12. NEURO-FUZZY BSP HIERARCHICAL SYSTEM FOR TIME FORECASTING AND FUZZY RULE EXTRACTION DOR DATA MINING APPLICATONS / SISTEMA NEURO-FUZZY HIERÁRQUICO BSP PARA PREVISÃO E EXTRAÇÃO DE REGRAS FUZZY EM APLICAÇÕES DE DATA MINING
This dissertation investigates the use of a Neuro-Fuzzy Hierarchical system for time series forecasting and fuzzy rule extraction for Data Mining applications. The objective of this work was to extend the Neuro-Fuzzy BSP Hierarchical model for the classification of registers and time series forecasting. The process of classification of registers in the Data
Publicado em: 2000