查詢教學大綱與進度

教學大綱與進度

課程基本資料：

學年期課號課程名稱階段學分時數修教師班級人撤備註

102-1 187452 英文科技論文寫作 1 3.0 3 ▲ 田方治工管所
工商博所 13 0 博班

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
102-1	187452	英文科技論文寫作	1	3.0	3	▲	田方治	工管所工商博所	13	0	博班

教學大綱與進度：

教師姓名田方治

Email fctien@ntut.edu.tw

最後更新時間 2013-09-09 10:35:41

課程大綱本課程旨在探討各種類型之神經網路，如逆傳遞網路，機率神經網路、學習向量量化網路、反傳遞網路、自述應共振理論、霍普菲爾網路、雙向聯想記憶網路、自組織映射圖網路等，此外相關文獻及運用之領域將深入討論。本學期將另加入基礎影像處理之概念及基本技術。

課程進度 Week Course Content Homework 1 Course Introduction and basic C programming 2 Topic 1: Introduction to ANN (Paper I—Fundamentals) and image processing (DIP) 3 Topic 2: Model and Architecture of ANN (Intro to MatLab) 4 Topic 3: Perceptron 6 Topic 4: Backpropagation (BP) (Paper IV- BP & GA) Project I 7 Topic 5: BP (cont’) (Paper V – BP & SA) (Paper VI – BP & PSO) 8 Topic 6: Radial Basis Function (Paper VI –Recurrent RBF) 9 Topic 7 K-means and VQ 10 Topic 8: Competitive Learning, SOFM 11 Topic 9: Hamming net and LVQ, Hopfield Network 12 Topic 10: CPN (Paper VII –CPN vs BP), ART (13 Topic 11: SVM 14 Topic 11: SVM other 15 Test (Other topics: SVM, SA, and Paper – Others) Presentation I 16 Presentation II – Paper reading 17 Presentation III— Paper reading 18 Final Project Presentation (Final Project Due)

評量方式與標準 Project ? I, II, III 45 % Assignment 25 % Exams 15 % Paper Study and Presentation 15 %

使用教材、參考書目或其他 【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】

使用外文原文書：　
TEXTBOOKS 􀂄 Satish Kumar, Neural Networks— a classroom approach, McGrawHill, 滄海書局 (Textbook) 􀂄 Simon, Haykin, Neural Network and Learning Machine, Pearson International 􀂄 羅華強，「類神經網路-MatLab 的應用」，清蔚科技出版 􀂄 葉怡成，「應用類神經網路」，儒林出版社 􀂄 Hagan, Demuth, Beale, Neural Network Design, ITP 新月圖書代理 􀂄 林昇甫，洪成安編著，「神經網路入門與圖樣辨識」，全華科技圖書 REFERENCE (Paper Reading List) The following papers will be assigned as paper reading for this course. Part of them will be cover during the lecture [1] Basheer, I.A., Hajmeer, M., 2000, Artificial neural networks: fundamentals, computing, design, and application, Journal of Microbiological Methods 43, 3-31 [2] Krycha, K.A., Wagner, U., 1999, Applications of artificial neural networks in management science: a survey, Journal of Retailing and Consumer Services 6, 185-203 [3] Bhattacharya, U., Parui, S.K., 1995, On the rate of convergence of perceptron learning, Pattern Recognition Letters 16, 491-497 [4] Juang, S.C., Tarng, Y.S., Lii, H.R., 1998, A comparison between the back-propagation and counter-propagation networks in the modeling of the TIG welding process, Journal of Materials Processing Technology 75, 54-62 [5] Zemouri, R., Racoceanu, D., Zerhouni, N., 2003, Recurrent radial basis function network for time-series prediction, Engineering Applications of Artificial Intelligence 16, 453-463 [6] Vesanto, J., Alhoniemi, E., 2000, Clustering of the self-organizing map, IEEE Transactions on Neural Networks 11 (3), 586-600 [7] Kuo, R.J., Su, Y.T., Chiu, C.Y., Chen, K.Y., Tien, F.C., 2006, Part family formation through fuzzy ART2 neural network, Decision Support System 42, 89-103

課程諮詢管道

備註

教師姓名	田方治
Email	fctien@ntut.edu.tw
最後更新時間	2013-09-09 10:35:41
課程大綱	本課程旨在探討各種類型之神經網路，如逆傳遞網路，機率神經網路、學習向量量化網路、反傳遞網路、自述應共振理論、霍普菲爾網路、雙向聯想記憶網路、自組織映射圖網路等，此外相關文獻及運用之領域將深入討論。本學期將另加入基礎影像處理之概念及基本技術。
課程進度	Week Course Content Homework 1 Course Introduction and basic C programming 2 Topic 1: Introduction to ANN (Paper I—Fundamentals) and image processing (DIP) 3 Topic 2: Model and Architecture of ANN (Intro to MatLab) 4 Topic 3: Perceptron 6 Topic 4: Backpropagation (BP) (Paper IV- BP & GA) Project I 7 Topic 5: BP (cont’) (Paper V – BP & SA) (Paper VI – BP & PSO) 8 Topic 6: Radial Basis Function (Paper VI –Recurrent RBF) 9 Topic 7 K-means and VQ 10 Topic 8: Competitive Learning, SOFM 11 Topic 9: Hamming net and LVQ, Hopfield Network 12 Topic 10: CPN (Paper VII –CPN vs BP), ART (13 Topic 11: SVM 14 Topic 11: SVM other 15 Test (Other topics: SVM, SA, and Paper – Others) Presentation I 16 Presentation II – Paper reading 17 Presentation III— Paper reading 18 Final Project Presentation (Final Project Due)
評量方式與標準	Project ? I, II, III 45 % Assignment 25 % Exams 15 % Paper Study and Presentation 15 %
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】使用外文原文書：　 TEXTBOOKS 􀂄 Satish Kumar, Neural Networks— a classroom approach, McGrawHill, 滄海書局 (Textbook) 􀂄 Simon, Haykin, Neural Network and Learning Machine, Pearson International 􀂄 羅華強，「類神經網路-MatLab 的應用」，清蔚科技出版 􀂄 葉怡成，「應用類神經網路」，儒林出版社 􀂄 Hagan, Demuth, Beale, Neural Network Design, ITP 新月圖書代理 􀂄 林昇甫，洪成安編著，「神經網路入門與圖樣辨識」，全華科技圖書 REFERENCE (Paper Reading List) The following papers will be assigned as paper reading for this course. Part of them will be cover during the lecture [1] Basheer, I.A., Hajmeer, M., 2000, Artificial neural networks: fundamentals, computing, design, and application, Journal of Microbiological Methods 43, 3-31 [2] Krycha, K.A., Wagner, U., 1999, Applications of artificial neural networks in management science: a survey, Journal of Retailing and Consumer Services 6, 185-203 [3] Bhattacharya, U., Parui, S.K., 1995, On the rate of convergence of perceptron learning, Pattern Recognition Letters 16, 491-497 [4] Juang, S.C., Tarng, Y.S., Lii, H.R., 1998, A comparison between the back-propagation and counter-propagation networks in the modeling of the TIG welding process, Journal of Materials Processing Technology 75, 54-62 [5] Zemouri, R., Racoceanu, D., Zerhouni, N., 2003, Recurrent radial basis function network for time-series prediction, Engineering Applications of Artificial Intelligence 16, 453-463 [6] Vesanto, J., Alhoniemi, E., 2000, Clustering of the self-organizing map, IEEE Transactions on Neural Networks 11 (3), 586-600 [7] Kuo, R.J., Su, Y.T., Chiu, C.Y., Chen, K.Y., Tien, F.C., 2006, Part family formation through fuzzy ART2 neural network, Decision Support System 42, 89-103
課程諮詢管道
備註