RESUMO
The construction of interpretable Takagi-Sugeno (TS) fuzzy models by means of clustering is addressed. First, it is shown how the antecedent fuzzy sets and the corresponding consequent parameters of the TS model can be derived from clusters obtained by the Gath-Geva (GG) algorithm. To preserve the partitioning of the antecedent space, linearly transformed input variables can be used in the model. This may, however, complicate the interpretation of the rules. To form an easily interpretable model that does not use the transformed input variables, a new clustering algorithm is proposed, based on the expectation-maximization (EM) identification of Gaussian mixture models. This new technique is applied to two well-known benchmark problems: the MPG (miles per gallon) prediction and a simulated second-order nonlinear process. The obtained results are compared with results from the literature.
RESUMO
A novel framework for fuzzy modeling and model-based control design is described. The fuzzy model is of the Takagi-Sugeno (TS) type with constant consequents. It uses multivariate antecedent membership functions obtained by Delaunay triangulation of their characteristic points. The number and position of these points are determined by an iterative insertion algorithm. Constrained optimization is used to estimate the consequent parameters, where the constraints are based on control-relevant a priori knowledge about the modeled process. Finally, methods for control design through linearization and inversion of this model are developed. The proposed techniques are demonstrated by means of two benchmark examples: identification of the well-known Box-Jenkins gas furnace and inverse model-based control of a pH process. The obtained results are compared with results from the literature.
RESUMO
The authors present a case of a 73 year old female patient. She had a left side pleural mesothelioma and had been operated thrice. At the third operation an abdominal propagation joined to the thoracic mesothelioma. Starting from this third surgery severe hypoglycemic episodes illustrated the clinical pictures. The electronmicroscopy of the resected tumor mass documented the presence of neurosecretory granules. This strongly suggests that tumor mass produced a substance which could have been responsible for the hypoglycemic episodes. Due to the fact the insulin levels measured during severe hypoglycemias were always on a low level, we suppose that an insulin like material (IGF--II) was responsible for the above mentioned symptoms. The authors briefly review the literature.