Computer Simulations and Modelling D

1. Explain the structure and operation of the technical and organizational system (military and civil).
2. Formulate a mathematical or experimental model of production, logistics and organizational systems.
3. Suggest appropriate methods for predicting the characteristics of technical and organizational systems.
4. Apply simulation in solving problems in manufacturing, logistics and organizational systems.
5. Evaluate the chosen modeling method and simulation results in relation to the behavior and properties of the real system.
6. Explain the application of the artificial intelligence method.
7. Choose and apply suitable scenario of events on the battlefield.
8. Evaluate and critically analyze the results of the battlefield simulation.

Classical ex cathedra lecture.

PC Lab exercizes.

1. Introduction to systems theory, modeling and simulation. Definitions and basic elements of technical and organizational systems and processes (military and civil).
2. Tasks and Modeling Objectives. Experimental and theoretical models. Preparation and development of the model.
3. Computer simulations based on analytical and numerical models. Verification of computer simulations.
4. Introduction to Game Theory. Basic elements, types and strategies of the game.
5. Statistical analysis of the process. Statistical modeling and regression analysis.
6. Modeling using experiment planning method.
7. Mid term 1
8. Discrete Events Simulation (DES). Enterprise Dynamics10 (ED10) and DES objects. Connecting DES objects, defining attributes, and managing events.
9. Methodology of DES modelling, examples. Trends in DES.
10. Simulations in military education and training.
11. The concept and capabilities of the VBS computer system. Operator training.
12. JCATS Concept and Capabilities. The role of the doctors in the exercise of the exercise. The role of the operator and staff of the units in exercising the exercises.
13. The basics of intelligent systems. Expert systems based on rules. Fuzzy expert systems.
14. Artificial Neural Networks. Basics of genetic algorithms and genetic programming. The basics of machine learning and data mining.
15. Mid term 2