Supplementarity measures on fuzzy sets.

In this paper, we commence the study of the so called supplementarity measures. They are introduced axiomatically and are then related to incompatibility measures by antonyms. To do this, we have to establish what we mean by antonymous measure. We then prove that, under certain conditions, supplementarity and incompatibility measuresare antonymous. Besides, with the aim of constructing antonymous measures, we introduce the concept of involution on the set made up of all the ordered pairs of fuzzy sets. Finally, we obtain some antonymous supplementarity measures from incompatibility measures by means of involutions.

Methodology for adapting the parameters of a fuzzy system using the extended Kalman filter

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

Application of the Extended Kalman filter to fuzzy modeling: Algorithms and practical implementation

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

The Threads of Biosystems Engineering

The core concepts, or threads, of Biosystems Engineering (BSEN) are variously understood by those within the discipline, but have never been unequivocally defined due to its early stage of development. This makes communication and teaching difficult compared to other well established engineering subjects. Biosystems Engineering is a field of Engineering which int egrates engineering science and design with applied biological, environmental and agricultural sciences. It represents an evolution of the Agricultural Engineering discipline applied to all living organisms not including biomedical applications. The basic key element for the emerging EU Biosystems Engineering program of studies is to ensure that it offers essential minimum fundamental engine ering knowledge and competences . A core curriculum developed by Erasmus Thematic Networks is used as benchmark for Agr icultural and Biosystems Engineering studies in Europe. The common basis of the core curriculum for the discipline across the Atlantic , including a minimum of competences comprising the Biosystems Engineering core competencies, has been defined by an Atlan tis project , but this needs to be taken further by defining the threads linking courses together. This paper presents a structured approach to define the Threads of BSEN . The definition of the mid-level competences and the associated learning outcomes has been one of the objectives of the Atlantis programme TABE.NET. The mid-level competences and learning outcomes for each of six specializations of BSEN are defined while the domain-specific knowledge to be acquired for each outcome is proposed. Once the proposed definitions are adopted, these threads will be available for global development of the BSEN.