系統建模與模擬
Systems Modeling & Simulation
| 節 | 週二 |
|---|---|
A 18:30–19:20 | 系統建模與模擬 EE210 3 節連堂 |
B 19:30–20:20 | |
C 20:30–21:20 |
* 根據陽明交大上課時間表所列
本課程重點是動態系統的建模、模擬、估測和最佳化。 本課程教授的概念包括基於物理和經驗的建模、動態建模、機器學習與無模型強化學習。以數學方式描述時間演化系統方法、系統建模方法和機電工程系統控制器設計、機器學習的分類和回歸及演算法。 The focus of this course is on modeling, simulation, estimation, and optimization of dynamic systems. Concepts taught in this course include physics-based and empirical modeling, dynamic modeling, machine learning and model-free reinforcement learning. For this purpose, the methods to mathematically describe time-evolving systems, the method of system modeling and design of controller for mechanical and electrical engineering systems, machine learning classification and regression are applied. 課程目標: 培養學生使用基於物理和機器學習的模型動態系統的理論和應用。 能夠利用不同建模方法給予系統適當的動態描述。 使學生具備分析工程問題的能力,運用工程知識和工程模擬程序解決問題。 學生將能夠收集和分析時間序列數據,以構建數據驅動的系統控制策略。 學生將能夠制定和執行一個項目,該項目利用機器學習中的課程主題和針對新應用程序的優化方法。 To train students in the theory and application of modeling dynamical systems using physics and machine learning. Students will be able to use different modeling methods to give appropriate dynamic description to the system. Enable students to have the ability to analyze engineering problems, and use engineering knowledge and engineering analysis simulation programs to solve problems. Students will be able to collect and analyze time-series data to construct data-driven system control strategies. Students will be able to formulate and execute a project that utilizes course topics and optimization methods for new applications in machine learning.
微積分、工程數學。 C++、Matlab、Labview程式語言
課後作業及平時學期課中表現 Exercise & Attendance : 20% 期中考 Quiz 1: 40% 期末考Final quiz: 40%
- L1: Introduction to Dynamic System Modeling
- L2: Time Domain Model of Systems
- L3: Frequency Domain Model of Systems
- L4: Neural Network Modeling, Simulation, and Control
- L5: Machine Learning Modeling a: 機器學習建模: 音樂情緒辨識與分類 Machine Learning Modeling : Music emotions recognition b: 用於損傷檢測與瑕疵分類的機器學習系統建模與分類 Machine learning algorithms for defect classification and damage detection under operational and environmental variability
- L6: Reinforcement Learning, Simulation, and Control
- L7: Modeling and Simulation of Flight Control System
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction of Dynamical Systems Modeling and Simulation |
| 第 2 週 | Introduction of Dynamical Systems Modeling and Simulation -Modeling and Control of Dynamical Systems -Introduces a few Controlled Dynamical Systems 1. 光學物鏡致動器建模,模擬,與控制 2. 機器學習系統建模與模擬 2-1. 結合時變情緒軌跡界面之機器學習自動音樂情緒追蹤系統 2-2. 以機器學習演算之風機葉片損傷偵測建模與模擬 2-3. Reinforcement Learning 強化學習在無人飛行器之建模,模擬,與飛行控制 3. 基於反應輪的衛星姿態穩定平台系統建模,模擬,與控制 4. PID Neural Network建模,模擬,與溫度控制 |
| 第 3 週 | Introduction of Dynamical Systems Modeling and Simulation -Introduces a few Controlled Dynamical Systems 1. 光學物鏡致動器建模,模擬,與控制 2. 機器學習系統建模與模擬 2-1. 結合時變情緒軌跡界面之機器學習自動音樂情緒追蹤系統 2-2. 以機器學習演算之風機葉片損傷偵測建模與模擬 2-3. Reinforcement Learning 強化學習在無人飛行器之建模,模擬,與飛行控制 3. 基於反應輪的衛星姿態穩定平台系統建模,模擬,與控制 4. PID Neural Network建模,模擬,與溫度控制 |
| 第 4 週 | Time Domain Modeling 系統時域建模 |
| 第 5 週 | 假日 |
| 第 6 週 | Frequency Domain Modeling 系統頻域建模 |
| 第 7 週 | Neural Network Modeling, Simulation, and Control 神經網路建模與模擬 |
| 第 8 週 | Neural Network Modeling, Simulation, and Control 神經網路建模與模擬 |
| 第 9 週 | 期中考 |
| 第 10 週 | Machine Learning Modeling 機器學習建模與模擬 -Supervised Machine Learning Modeling 監督式機器學習建模,模擬,與應用實例 |
| 第 11 週 | Unsupervised Machine Learning Modeling 無監督式機器學習建模,模擬,與應用實例 |
| 第 12 週 | Reinforcement Learning Modeling, Simulation, and Control 強化學習建模,模擬,與控制 |
| 第 13 週 | Reinforcement Learning Modeling, Simulation, and Control 強化學習建模,模擬,與控制 -基於強化學習軌跡生成的無人飛行器 全方位自主垂直棲息系統建模,模擬,與控制 Omni-Directional Autonomous Perching of Unmanned Aerial Vehicle using Reinforcement Learning Trajectory Generation, Modeling, and Simulation |
| 第 14 週 | Reinforcement Learning Modeling, Simulation, and Control 強化學習建模,模擬,與控制 -多軸組態無人飛行器之強化學習系統建模,模擬,與控制 Reinforcement Learning for Multi-Rotor Configuration UAV |
| 第 15 週 | Flight System Modeling, Simulation, and Control 飛行系統建模,模擬,與控制 |
| 第 16 週 | Flight System Modeling, Simulation, and Control 飛行系統建模,模擬,與控制 學期考試 |
David L. Smith., ”Introduction to Dynamic Systems Modeling for Design”, Prentice Hall. Simon Haykin. "Neural Networks and Learning Machines", Pearson. StevenL. Brunton, J. Nathan Kutz, "Data-Driven Science and Engineering machine Learning, Dynamical Systems, and Control", 2nd edition, Cambridge
- 聯絡方式
- chengstone@nycu.edu.tw