Present state and new problems of further GMDH development
Systems Analysis Modelling Simulation
Self-Organizing Methods in Modeling: Gmdh Type Algorithms
Self-Organizing Methods in Modeling: Gmdh Type Algorithms
Systems Analysis Modelling Simulation
Self-organising modelling as a part of simulation process
Systems Analysis Modelling Simulation - Special issue: Self-organising modelling and simulation
A mental workload predicator model for the design of pre alarm systems
EPCE'07 Proceedings of the 7th international conference on Engineering psychology and cognitive ergonomics
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This article presents a method of modeling based on a data set by means of the GMDH algorithms. The data set contains information about fresh students of the Faculty of Electrical Engineering, Czech Technical University of Prague, Czech Republic (CTU, FEE). The motivation for our investigation was to discover, whether and how a particular student will be successful in his/her university studies. The data set was mined from official study application forms. They reflect student's personality, type and results (grades) from high school and entrance examination. These results serve as input vectors for prediction of his/her study results after the 1st semester. The results produced by the model were then compared with the real ones. The second motivation for our investigation was to find the significance of a particular input vector component, because it enables us to identify possible weak points of a student. As appropriate tools the GMDH neural networks (both linear and nonlinear) have been used.