Design of adaptive, multidimensional information and control systems

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The sharp increase in data gathering (remote sensing, sensor networks, etc.), processing and networking capabilities of today's computing systems means that it is now possible to access vast amounts of data and to efficiently use them in the design and control of diversified, often complex systems. These developments have had a profound impact on the evolution of the area of intelligent information and control systems. In the proposed research program, the main objective is the development of an integrated methodology that addresses those issues in the design and control of adaptive, self-organizing systems that are critical in the context of distributed processing of large quantities of data in dynamic, uncertain environments. Specific long-term objectives include: - Development of theoretical and methodological foundations for the design of systems that adapt their structure by combining different types of information processing technique according to functional and performance requirements. - Addressing the requirements imposed by the fact of dealing with large amounts of multi-dimensional information (dimensionality reduction, performance criteria, etc.).   - Development of hybrid technologies integrating mainly neural networks, fuzzy logic, selected statistical techniques and adaptive indexing, and their application in non-linear, time-variable, uncertain systems, where the need for adaptability and learning capabilities is most pronounced. The results of this research program, while contributing to theoretical developments in the areas of control systems and pattern recognition, will at the same time be applicable in several diversified domains, such as remote sensing, geomatics, Intelligent Manufacturing Systems, distributed measurement and control systems, and decision support systems.