Complex system and structure modeling by data processing

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This research focuses on data-driven structure modeling. As computational technology advances in storing and processing large amounts of complex-structured data, there is an inevitable demand for fast transformation of the available data to proper information about its underlying structure. One important challenge in transforming data to structure is in order selection for parametric modeling. Over the past decades,  various parametric model complexity selection approaches have been proposed  and  I have recently developed a new approach for this problem. The new approach employs ideas of conventional system identification (with issues such as unmodeled dynamics) and computational learning theory, and also utilizes coding concepts of information theory to approach the model selection problem from a new perspective. These characteristics of the new approach allow the method to spread far beyond its initial applications. The proposed project focuses on related developments in theory, methodology, and application of the new complexity selection approach. As the control's modern view sees feedback systems as tools for uncertainty handling and management, it is becoming more and more essential to provide compatible methods of data-driven structure and uncertainty modeling. The main advantage of the new complexity selection approach is in its ability to quantify the structure's  uncertainty. The objectives of this research are to develop and expand the new approach for data-driven structure modeling in areas such as nonlinear structure modeling, adaptive and online system modeling and identification, and ARMAX modeling. The great strength of the new approach, from its consistent theory to its effective algorithms, promises a broad use of these methods in a variety of potential applications including biomedical system identification and modeling, bioinformatics, channel identification in communications, and economics.