Clustering of TS-fuzzy system

This paper presents a fuzzy c-means clustering method for partitioning symbolic interval data, namely the T-S fuzzy rules. The proposed method furnish a fuzzy partition and prototype for each cluster by optimizing an adequacy criterion based on suitable squared Euclidean distances between vectors of intervals. This methodology leads to a fuzzy partition of the TS-fuzzy rules, one for each cluster, which corresponds to a new set of fuzzy sub-systems. When applied to the clustering of TS-fuzzy system the result is a set of additive decomposed TS-fuzzy sub-systems. In this work a generalized Probabilistic Fuzzy C-Means algorithm is proposed and applied to TS-Fuzzy System clustering

Decomposition of a greenhouse TS-Fuzzy model by clustering process

This paper presents a fuzzy c-means clustering method for decompose a T-S fuzzy system. This technique is used to organize the fuzzy greenhouse climate model into a new structure more interpretable, as in the case of the physical model. This new methodology was tested to split the inside greenhouse air temperature and humidity flat fuzzy models into fuzzy sub-models. These fuzzy sub-models are compared with its counterpart’s physical sub-models. This algorithm is applied to the T-S fuzzy rules. The results are several clusters of rules where each cluster is a new fuzzy sub-system. This is a generalized Probabilistic Fuzzy C-Means (PFCM) algorithm applied to TS-Fuzzy System clustering. This allows automatic organization of one fuzzy system into a multimodel Hierarchical Structure.This work was supported by Fundação para a Ciência e Tecnologia (FCT) under grant POSI/SRI/41975/2001 and by CITAB - Centro de Investigação e Tecnologias Agro-Abientais e Biológicas

Clustering algorithms for fuzzy rules decomposition

This paper presents the development, testing and evaluation of generalized Possibilistic fuzzy c-means (FCM) algorithms applied to fuzzy sets. Clustering is formulated as a constrained minimization problem, whose solution depends on the constraints imposed on the membership function of the cluster and on the relevance measure of the fuzzy rules. This fuzzy clustering of fuzzy rules leads to a fuzzy partition of the fuzzy rules, one for each cluster, which corresponds to a new set of fuzzy sub-systems. When applied to the clustering of a flat fuzzy system results a set of decomposed sub-systems that will be conveniently linked into a Hierarchical Prioritized Structures

Probabilistic clustering algorithms for fuzzy rules decomposition

The fuzzy c-means (FCM) clustering algorithm is the best known and used method in fuzzy clustering and is generally applied to well defined set of data. In this paper a generalized Probabilistic fuzzy c-means (FCM) algorithm is proposed and applied to clustering fuzzy sets. This technique leads to a fuzzy partition of the fuzzy rules, one for each cluster, which corresponds to a new set of fuzzy sub-systems. When applied to the clustering of a flat fuzzy system results a set of decomposed sub-systems that will be conveniently linked into a Parallel Collaborative Structures

Probabilistic fuzzy clustering algorithm for fuzzy rules decomposition

The Fuzzy C-Means (FCM) clustering algorithm is the best known and the most used method for fuzzy clustering and is generally applied to well defined sets of data. In this work a generalized Probabilistic Fuzzy C-Means (PFCM) algorithm is proposed and applied to fuzzy sets clustering. The methodology presented leads to a fuzzy partition of the fuzzy rules, one for each cluster, which corresponds to a new set of fuzzy sub-systems. When applied to the clustering of a flat fuzzy system the result is a set of decomposed sub-systems that will be conveniently linked into a Parallel Collaborative Structure.This work was supported by Fundação para a Ciência e Tecnologia (FCT) under grant POSI/SRI/41975/2001 and CITAB (UTAD)

Sistema de medição e controlo de qualidade do ar interior

A crise do petróleo na década de 70 levou o homem a construir edifícios com melhor isolamento térmico e energeticamente mais eficientes. Apesar destas mudanças construtivas terem reflexos positivos em termos consumo de energia e de conforto térmico, reduziram substancialmente as taxas de ventilação natural e, consequentemente, agravaram a qualidade do ar interior (QAI) dos edifícios. O presente estudo teve como objetivo a construção de um monitor de qualidade do ar interior, usando a plataforma Arduíno. O sistema de monitorização permite avaliar quantitativamente as componentes CO2, CO, humidade relativa e temperatura do ar. O sistema pode integrar ainda um ventilador que é acionado em função das concentrações de CO2 e/ou CO, possibilitando a regulação dos níveis de QAI. O sistema de monitorização foi testado, com e sem regulação automática das taxas de ventilação, em 4 espaços (gabinetes) distintos, durante um período global de 12 dias. Os resultados mostraram que, na ausência de controlo automático da ventilação, os níveis de CO2 prevalecentes nos diferentes espaços estudados excederam frequentemente o limiar de proteção da saúde humana estabelecidos na lei (1250 ppm). Com a inclusão do mecanismo de controlo automático de ventilação (15 Watts, 93 m3/h), foi possível manter os níveis de dióxido de carbono abaixo dos níveis máximos recomendados, sendo, portanto, um bom indicador de prevalência de boas condições de QAI.info:eu-repo/semantics/publishedVersio

Time series prediction by perturbed fuzzy model

This paper presents a fuzzy system approach to the prediction of nonlinear time series and dynamical systems based on a fuzzy model that includes its derivative information. The underlying mechanism governing the time series, expressed as a set of IF–THEN rules, is discovered by a modified structure of fuzzy system in order to capture the temporal series and its temporal derivative information. The task of predicting the future is carried out by a fuzzy predictor on the basis of the extracted rules and by the Taylor ODE solver method. We have applied the approach to the benchmark Mackey-Glass chaotic time series.This work was supported by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) under grant POSI/SRI/41975/2001

Fuzzy identification and predictive control of the alcoholic fermentation process

In this work a fuzzy identification model for yeast growth applied to the specific case of alcoholic fermentation is presented. Two fuzzy techniques were applied, namely the designated Mamdani modelling and the TSK (Takagi Sugeno Kang) modelling. The results were compared with the ones obtained with a deterministic model proposed by Boulton. A predictive controller is also presented and the results obtained compared with the usual PID controller. The obtained results for the identification models and for the controller showed that both methodologies can be applied to biological processes

Greenhouse air temperature optimal fuzzy controller

A new scheme of fuzzy optimal control for the temperature of an Agriculture Greenhouse is presented. The proposed method is based on the Pontryagin’s Minimum Principle (PMP) that is used to train an adaptive fuzzy inference system to estimate values for the optimal co-state variables. This work shows that it is possible to successfully control a greenhouse by using these techniques. A method is presented to control the greenhouse air temperature achieving significant energy savings by minimizing a quadratic performance index selected for the desired operating conditions. This approach allows finding a solution to the optimal control problem on-line by training the system, which can be used on a closedloop control strategy. Successful simulations results for the controlled system are presented

Segmentação de imagens fetais com potencial para desenvolvimento de ferramentas de apoio ao diagnóstico

Programa Doutoral em Engenharia Eletrónica e de ComputadoresDurante uma gravidez é aconselhável a realização de 3 exames ecográficos. O primeiro, e reconhecido pelos especialistas como mais importante, é o do primeiro trimestre. Neste exame, realizado entre as 11 e as 14 semanas, é possível avaliar a idade gestacional, o desenvolvimento fetal e, mais importante, as anomalias fetais. Na avaliação das anomalias fetais incluem-se as cromossómicas, que são detetáveis a partir da observação da medida da Translucência da Nuca mas que deve ser cruzada com a medida da Distância Crânio-Caudal e a idade materna. As medidas são retiradas manualmente e os seus valores variam com a disponibilidade física e a motivação do operador, pelo que os resultados mostram variabilidade intra e inter-operador. As imagens recolhidas pelos sistemas de aquisição baseados em ultrassons apresentam pouco detalhe, baixo contraste, baixa relação sinal/ruído e grande variabilidade morfológica que dificulta a tarefa de segmentação e, consequentemente, o desenvolvimento de sistemas de medição automáticos. Como tal, o seu tratamento exige a utilização de técnicas que reúnam características adequadas e que permitam o desenvolvimento de sistemas robustos. Este trabalho trata a questão da extração automática da medida da Distância Crânio-Caudal (DCC) a partir das imagens de ultrassons habitualmente usadas para este fim. Para tal, propõe a utilização de técnicas de Fuzzy Clustering, de Contornos Ativos e de Aprendizagem Máquina, nomeadamente SVMs, para a segmentação das imagens com vista à identificação do corpo do feto. Estas abordagens potenciaram a formulação de novos modelos que permitem enfrentar as dificuldades inerentes ao tratamento deste tipo de imagens. São também propostas metodologias automáticas de extração da medida DCC, sendo que algumas delas dependem dos processos de segmentação sugeridos. Os resultados obtidos para a medida da DCC apresentam um erro absoluto médio relativo dentro dos intervalos de variabilidade inter-operador referidos na literatura.During pregnancy it is advisable to conduct 3 ultrasound examinations. The first and most important is performed in the first trimester. In this exam, done between the 11th and 14th week, the gestational age, the fetal development and, most importantly, the fetal abnormalities can be assessed. The assessment of fetal anomalies include chromosomal, which are detectable from observation measuring the nuchal translucency size. However it should be crossed with a measure of the crown-rump length and the maternal age. These measures are manually performed and their values vary with the physical availability and motivation of the operator, so the results show intra and inter-operator variability. The images collected by acquisition systems based on ultrasounds have little detail, low contrast, low signal/noise ratio and great morphological variability which difficult the segmentation task and the development of automatic measuring systems. Because of these reasons, ultrasound image processing requires the use of techniques that meet appropriate characteristics and that enable the development of robust systems. This work treats the subject of automatic extraction of the crown-rump length from ultrasound images commonly used for this purpose. It uses Fuzzy Clustering, Active Contours and Machine Learning techniques for the segmentation of images in order to identify the fetal body. These approaches promoted the development of new models that allow face the inherent difficulties in treating this type of images. Methods for the crown-rump length automatic measurement are also proposed, some of which depend on the suggested segmentation methods. The results obtained for the crown-rump length presented a relative mean absolute error within inter-operator variability ranges reported in the literature