| 當期課號 |
7796 |
Course Number |
7796 |
| 授課教師 |
謝政勳 |
Instructor |
HSIEH,CHENG HSIUNG |
| 中文課名 |
類神經網路 |
Course Name |
Neural Networks and Its Applications |
| 開課單位 |
資訊工程系碩士在職專班一A |
Department |
|
| 修習別 |
選修 |
Required/Elective |
Elective |
| 學分數 |
3 |
Credits |
3 |
| 課程目標 |
Fundamental concepts and models of artificial neural systems
Single-layer perception classifiers
Multilayer feedforward networks
Associative memories
Matching and self-organizing networks
Applications of neural algorithms and systems
Neural networks implementation |
Objectives |
Fundamental concepts and models of artificial neural systems
Single-layer perception classifiers
Multilayer feedforward networks
Associative memories
Matching and self-organizing networks
Applications of neural algorithms and systems
Neural networks implementation |
| 教材 |
1. M. T. Hagan, H. B. Demuth, Neural Network Design, Thomson Learning, 1996. (新月圖書代理) 2.S. Haykin, Neural Networks-A Comprehensive Foundation, 2nd Edition, Pretice Hall, 1999. (開發圖書代理) |
Teaching Materials |
|
| 成績評量方式 |
期中考30%，期末專題30%，作業20%，專題口頭報告20% |
Grading |
Midterm Exam. 30%, Project 30%, Homeworks 20%, Oral Presentation 20% |
| 教師網頁 |
|
| 教學內容 |
1. Introduction Perceptron
2. Widrow-Hoff Learning Backpropagation
3. Associative Learning Competitive Networks
4. Adaptive Resonance Theory (ART) Hopfield Network
5. Recurrent Neural Networks
6. Neural Networks with Other AI Schemes (e.g. GA, Fuzzy)
7. Applications of Neural Networks |
Syllabus |
1. Introduction Perceptron
2. Widrow-Hoff Learning Backpropagation
3. Associative Learning Competitive Networks
4. Adaptive Resonance Theory (ART) Hopfield Network
5. Recurrent Neural Networks
6. Neural Networks with Other AI Schemes (e.g. GA, Fuzzy)
7. Applications of Neural Networks |