"MATLAB and Scientific Computation" is a professional course for engineering majors. MATLAB is a computer software that focuses on numerical calculations and data graphics, and includes professional software packages suitable for multiple disciplines, as well as complete program development functions. Through the study of this course, students are required to master MATLAB data types, matrix input and operation methods, grammatical structures, the use of functions, as well as basic and advanced professional knowledge such as two-dimensional and three-dimensional drawing, Simulink simulation, regression analysis, and intelligent algorithm applications, and be able to skillfully apply MATLAB in professional course learning and design, solve complex mathematical calculation problems in related courses, and cultivate students' ability to use MATLAB as a tool to analyze and solve practical problems.
Overview
Syllabus
- Charpter 1 MATLAB Introduction
- 1.1 The introduction of MATLAB environment
- 1.2 Data types and basic operations
- Charpter 2 The Matrix and Its Operation
- 2.1 The creation of the Matrix
- 2.2 The modification of the Matrix
- 2.3 The basic operations of Matrix
- 2.4 The analysis of the Matrix
- Charpter 3 MATLAB Program Structure and M file
- 3.1 Sequence program structure
- 3.2 M file
- 3.3 M function file
- Charpter 4 Polynomial Operation and Data Processing
- 4.1 Polynomial operation
- 4.2 Data interpolation and fitting
- 4.3 Data statistics
- 4.4 Numerical calculation
- Charpter 5 Language Symbol Operation
- 5.1 Creation and application of MATLAB symbolic objects
- 5.2 Symbolic polynomial function operation
- Charpter 6 Data Visualization
- 6.1 2D curves and graphs
- 6.2 2D curves format setting
- 6.3 2D Special Drawing
- 6.4 3D curves and surfaces
- Charpter 7 Simulink Simulation Foundation
- 7.1 Overview of Simulink
- 7.2 The application of Simulink
- 7.3 Simulink simulation
- Charpter 8 Regression Analysis
- 8.1 Principle of least squares method
- 8.2 Linear Regression and Its MATLAB Applications
- 8.3 Non-Linear Regression and Its MATLAB Application
- Charpter 9 Genetic Algorithm and Its MATLAB Implementation
- 9.1 The Principle of Genetic Algorithm
- 9.2 The Application of Genetic Algorithm in MATLAB
- Charpter 10 Artificial Neural Network and Its MATLAB Implementation
- 10.1 Basic concepts of neural networks
- 10.2 The principle of BP neural network
- 10.3 The Application of BP Neural Network in MATLAB
- Charpter 11 Deep learning and Its MATLAB Implementation
- 11.1 Basic concepts of deep learning
- 11.2 Introduction to convolutional neural network (CNN)
- 11.3 CNN algorithm and its MATLAB implementation
- Chapter 12 Reinforcement learning and Its MATLAB Implementation
- 12.1 The principles of Reinforcing Learning (RL)
- 12.2 An introduction to the main algorithms for reinforcement learning
- 12.3 The application of Reinforcement learning in MATLAB
- Charpter 13 Adanced simulink applications
- 13.1 Simulink and Materab Interface Technology
- 13.2 Design and adjustment of PID controller I
- 13.3 Design and adjustment of PID controller II
- 13.4 Simulink Simulation of Dynamic Systems
- 13.5 Simulink Simulation Technology
- 期末考试
Taught by
Ni Yanting
