Browsing by Author "Salgado, Paulo"
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- Clustering algorithms for fuzzy rules decompositionPublication . Salgado, Paulo; Igrejas, GetúlioThis 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.
- Clustering of TS-fuzzy systemPublication . Igrejas, Getúlio; Salgado, PauloThis 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 processPublication . Salgado, Paulo; Igrejas, GetúlioThis 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.
- Evolutionary based on selfish and altruism strategies - an approach to path planning problemsPublication . Salgado, Paulo; Igrejas, Getúlio; Afonso, Paulo Alexandre FerreiraThis paper presents a novel hybrid optimization appproach based on a genetic algoritm that combines selfish gene and altruism view of evolution.
- Fuzzy clustering for segmantation of 1st trimester ultrasound fetal imagesPublication . Igrejas, Getúlio; Salgado, Paulo; Couto, CarlosThe work herein presented is a part of a broader set of tasks included in a PhD thesis which main objective is to develop an automatic measurement system for the crown-rump, nuchal translucency and biparietal measurements in ultrasound fetal images. These measurements are of extreme importance to evaluate the possible abnormal conditions of the fetus, namely chromosomal anomalies like Down’s syndrome, also known as Trisomy 21. To achieve this objective the task of segmentation, which consists inidentifying the relevant objects/structures in the ultrasound images and separate them from the non relevant ones, is of utmost importance. In this work different fuzzy clustering approaches for segmenting 1st trimester ultrasound fetal images are presented and applied for the crown-rump measurement. Results are compared with other methodologies to evaluate their performance.
- Fuzzy identification and predictive control of the alcoholic fermentation processPublication . Igrejas, Getúlio; Salgado, Paulo; Couto, CarlosIn 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 controllerPublication . Salgado, Paulo; Igrejas, Getúlio; Cunha, José BoaventuraA 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.
- Hierarchization process by possibilistic fuzzy clustering of fuzzy rulesPublication . Salgado, Paulo; Cunha, Manuela; Pavão, João; Igrejas, GetúlioThis paper presents a possibilistic fuzzy clustering algorithm that is applied to a multidimensional fuzzy set or fuzzy rules. This method can be used to decompose the fuzzy system into an hierarchical structure. 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. This technique is tested to organize the fuzzy medel into a new and more comprehensive structure.
- Hierarchization process by possibilistic fuzzy clustering of fuzzy rulesPublication . Salgado, Paulo; Cunha, Manuela; Pavão, João; Igrejas, GetúlioThis paper presents a possibilistic fuzzy clustering algorithm that is applied to a multidimensional fuzzy set or fuzzy rules. This method can be used to decompose the fuzzy system into an hierarchical structure. 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. This technique is tested to organize the fuzzy model into a new and more comprehensive structure.
- Hierarchization process by possibilistic fuzzy clustering of fuzzy rulesPublication . Salgado, Paulo; Cunha, Manuela; Pavão, João; Igrejas, GetúlioThis paper presents a possibilistic fuzzy clustering algorithm that is applied to a multidimensional fuzzy set or fuzzy rules. This method can be used to decompose the fuzzy system into an hierarchical structure. 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. This technique is tested to organize the fuzzy model into a new and more comprehensive structure.
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