The generalized matrix equation for systematic evaluation of oral Russian self-study

Author:

Li Shu1,Zhi Boyu2

Affiliation:

1. 1 College of Foreign Languages , Shenyang University , Shenyang, Liaoning, 110044 , China .

2. 2 Student Majoring in Data Science and Big Data Technology, Shenyang Urban Construction .

Abstract

Abstract Generalized Lyapunov matrix equations appear in the fields of controllability analysis and model reduction of bilinear systems, stability analysis and optimal stabilization of linear stochastic systems, etc. The author studies the numerical solution of the generalized Lyapunov matrix equation combined with the evaluation of the Russian spoken language self-study system. Taking the empirical analysis as the research object, we selected 120 oral test samples from 12 colleges and universities in the 2020 national four-level unified test of Russian majors, compared with the teaching syllabus and the examination syllabus, the data were analyzed from the perspectives of speech speed, vocabulary, grammar and vocabulary errors, and a series of important conclusions were drawn, according to the specific problems exposed by the students in the oral test, the current situation of oral Russian teaching is discussed from the aspects of oral teaching materials, oral teachers and oral teaching methods.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Engineering (miscellaneous),Modeling and Simulation,General Computer Science

Reference10 articles.

1. Tian, Y., Liu, X., & Yuan, S. F. (2021). On Hermitian Solutions of the Generalized Quaternion Matrix Equation AXB + CXD = E. Mathematical Problems in Engineering: Theory, Methods and Applications, 2021, 23(Pt.53).

2. Yu, C., Liu, X., & Zhang, Y. (2020). The generalized quaternion matrix equation A X B + C X D = E. Mathematical Methods in the Applied Sciences, 14(2), 25.

3. Boonruangkan, N., & Chansangiam, P. (2020). Gradient Iterative Method with Optimal Convergent Factor for Solving a Generalized Sylvester Matrix Equation with Applications to Diffusion Equations. Multidisciplinary Digital Publishing Institute, 75(10), 36.

4. Li, J., Zhou, H., Li, Y., et al. (2021). A Memristive Neural Network Based Matrix Equation Solver with High Versatility and High Energy Efficiency. SCIENCE CHINA Information Sciences, 26(3), 45.

5. Dehghan, M., & Shirilord, A. (2020). Matrix multisplitting Picard-iterative method for solving generalized absolute value matrix equation. Applied Numerical Mathematics, 158, 425-438.

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