The training system is implemented with the use of the fuzzy classifier that represents fuzzy knowledge base, the input of which receives signals about current state of the traction rolling stock and of the environment. The results of the work allow implementing intelligent DSS in modern locomotives. This will enhance the level of safety and efficiency of driving a train.
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- 1. Introduction
- 2. Literature review and problem statement
- 3. The aim and tasks of the study
- 4. Methods and tools for designing locomotive decision support systems
- 4. 1. Structure and architectural hierarchy of DSS for locomotive crews
- 4. 2. Formalization of fuzzy situations in the process of train driving
- 4.3. The models that simulate decision making processes
- 4.4. Methods of decision making of locomotive DSS under conditions of uncertainty of the input data
- 4. 5. Determining a current state of train as a control object
- 4. 6. The basics of the system of self— learning of intelligent locomotive DSS
- 4. 7. Development of mathematical model of dynamic knowledge base
- 5. Discussion of results of research into intelligent locomotive DSS
- 6. Conclusions