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Research of Methods for Significant Accereation of Parameter Estimation of Simulation Models
Appel, Martin ; Opluštil, Vladimír (referee) ; Křivánek, Václav (referee) ; Grepl, Robert (advisor)
The thesis focuses on achieving a significant speed-up in the estimation of simulation model parameter values. This is achieved through the appropriate choice of a solver that is computationally less demanding, but at the same time has an acceptable error in the range of parameter values, through the use of distribution of computations on parallel threads, heuristic methods for reducing the space of parameter values, and modification of optimization methods. In this thesis, the research related to the objectives of the thesis is first discussed, then the exact objectives of this thesis are stated and the implementation of each objective is described in separate chapters. The results of this thesis include a tool used to analyze solver choice, an analysis of parallel thread efficiency, a parallel simulation distribution tool, nine modified optimization methods, and a new tool for estimating parameter values. Finally, the results obtained are evaluated.
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New Hybrid Methods for Robust and Automated Parameter Estimation of Mechatronic Systems
Najman, Jan ; Štefek, Alexandr (referee) ; Opluštil, Vladimír (referee) ; Grepl, Robert (advisor)
The thesis deals with the development of a new hybrid optimization algorithm for mechatronic models. The introductory chapters are devoted to a general description of the problem of estimating unknown system parameters, based on the developed mathematical model and measured data. Furthermore, an overview and a brief description of available optimization algorithms that are suitable for solving this type of problem is given. Based on the research study, the specific objectives of the paper are then formulated. In the second part of the thesis, a newly developed set of mechatronic models created using physical modelling tools is described. Subsequently, a comparative test of the speed and success rate of the selected optimization algorithms is performed using these models. Based on the results of this test, the design of a new hybrid algorithm is proposed, which is then tested and compared with the other algorithms. Finally, several new auxiliary functions and tools are presented to detect and analyze improperly designed parameter estimation problems.
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