Artículo de publicación ISIParietaria pollen has never been considered as a significant cause of pollinosis in Chile; therefore, the sensitization to Parietaria study has never been included in the study of patients with clinical suspicion of pollinosis in this region. The objective of this study was to describe the clinical characteristics of pollinosis caused by Parietaria in the Valparaı´so region, related to air concentrations of this kind of pollen. A cross-sectional studywas performedin the city of Valparaı´so. It consisted of two stages: In the first, pollen grains were counted between 1999 and 2001. In the second, a sensitization profile on a patient population diagnosed with ARC (allergic rhinoconjunctivitis) was evaluated. Parietaria judaica (P. judiaca) presented pollination all year long, with aggravation in the spring and summer, and with values reaching 80 grains/m3 (weekly average). These findings determined the transience of the symptoms in this population, which is mainly perennial with seasonal aggravations. A total of 72 atopic subjects were obtained during the whole sample recollection period. P. judaica was the second most frequent cause of sensitization (60 %) after Dermatophagoides in the sample overall. Also, in monosensitized subjects, it was the first cause of pollen sensitization. P. judaica represents the second cause of allergy in Valparaı´so and the first cause of pollinosis. These findings suggest the importance of quantifying Parietaria in Valparaı´so and near cities, plus investigating the presence of sensitization and symptoms to allergies in a significant proportion of patients in this region

Modeling phase is fundamental both in the analysis process of a dynamic system and the design of a control system. If this phase is in-line is even more critical and the only information of the system comes from input/output data. Some adaptation algorithms for fuzzy system based on extended Kalman filter are presented in this paper, which allows obtaining accurate models without renounce the computational efficiency that characterizes the Kalman filter, and allows its implementation in-line with the process.

When we try to analyze and to control a system whose model was obtained only based on input/output data, accuracy is essential in the model. On the other hand, to make the procedure practical, the modeling stage must be computationally efficient. In this regard, this paper presents the application of extended Kalman filter for the parametric adaptation of a fuzzy model.

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