1. Fundamentals of the dynamic system analysis, comparison of the analytical and experimental methods of the identification.
2. Linear and nonlinear systems. Linearization of models.
3. Time varying systems, time invariant systems, systems with time delay, equilibrium, stability of the equilibrium.
4. Programming of the models in the form of differential equations and transfer functions.
5. Realization of the mathematical models using the simulation programmes. Classification of the simulation programmes.
6. Numerical methods used for the modelling of the static characteristics.
7. Numerical methods for integration and derivative computation.
8. Numerical methods for solution of the differential equations.
9. State space models – numerical solution. Transition matrix.
10. Stability of the methods for the numerical solution of the differential equations.
11. A-stabil, AD-stabil methods of the numerical solution of the differential equations.
12. Discrete event systems. Structure, description, modelling and simulation.
13. Random number generation, Monte Carlo methods for discrete event system modelling.
14. Simulation programmes. Case study.
2. Linear and nonlinear systems. Linearization of models.
3. Time varying systems, time invariant systems, systems with time delay, equilibrium, stability of the equilibrium.
4. Programming of the models in the form of differential equations and transfer functions.
5. Realization of the mathematical models using the simulation programmes. Classification of the simulation programmes.
6. Numerical methods used for the modelling of the static characteristics.
7. Numerical methods for integration and derivative computation.
8. Numerical methods for solution of the differential equations.
9. State space models – numerical solution. Transition matrix.
10. Stability of the methods for the numerical solution of the differential equations.
11. A-stabil, AD-stabil methods of the numerical solution of the differential equations.
12. Discrete event systems. Structure, description, modelling and simulation.
13. Random number generation, Monte Carlo methods for discrete event system modelling.
14. Simulation programmes. Case study.