Experience of application of education course “Neural network modeling of complex technical systems” in the student’s scientific and research work

Abstract

In this paper we consider the application of education course “Neural Network modeling of Complex Technical Systems” in the student’s scientific and research work. Student’s scientific work is an obligatory part of education in the Chair of Electronic Technologies in Mechanical Engineering of Bauman Moscow State Technical University. Besides successful research depends on confided usage of common methods, algorithms and tools of statistical community special attention in degree programs is given to data analysis and modeling methods. For engineers and nanoscientists neural network models have become a powerful tool of scientific research. The course “Neural Network modeling of Complex Technical Systems” is taught in the second year of the Master’s programs “Electronics and Nanoelectronics” and “Nanoengineerig”. After completing the study of the discipline the student are expected to understand neural networks algorithms; know modern neural networks software products; be capable to prepare data, design and train neural network and to apply neural networks algorithms in practice. The course has rather practical than theoretical nature, it plays a significant role in students research work. During the homework students learn to create, tune and train their own neural networks and get full and detailed understanding of research in computer vision. Neural Networks models are successfully used in students scientific and research projects and presented in graduation works. In this paper we presented few examples of how neural networks are used in students projects.