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 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

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

A neural network based fall detector

In this project we present an intelligent fall detector system based on a 3-axis accelerometer and a neural network model that allows recognizing several possible motion situations and performing an emergency call only when a fall situation occurs, with low false negatives rate and low false positives rate. The system is based on a two module platform. The first one is a Mobile Station (MS) and should be carried always by the person. An accelerometer is implemented in this module and its information is transferred via a radio-frequency channel (RF) to the Base Station (BS). The BS is fixed and is connected to a GSM (Global System for Mobile communication) module. A neural network model was built into the BS and is able to identify falls from other possible motion situations, based on the received information. According to the neural network response the system sends a SMS (Short Message Service) to a destination number requesting for assistance