國立成功大學電機系

－王振興教授－

開授課程及大綱

科目名稱	類神經網路簡介
學分數	3
開課學期	100學年度第一學期
教學目標	本課程提供學生深入理解類神經網路領域中的重要概念，分散式智慧及適應性系統的設計，此外，本課程將帶領同學實際應用類神經網路於不同領域中，如圖訊辨別、系統鑑定、非線性預測及控制等 。
教學大綱	Introduction History Artificial intelligence and neural networks   Models of neurons Network architectures Learning Methods Supervised learning  Unsupervised learning Multiple Layer Perceptrons First order and second order optimization algorithms   Structure/Parameter Learning  Effects of /Heuristics for user predefined parameters Examples/Homework Kohonen Self-Organizing Maps Self-organizing map The SOM algorithm Learning vector quantization Kernel-based Networks Radial-basis function networks Interpolation/Regularization Learning strategies Support vector machines Support vector clustering Dynamically Driven Recurrent Networks Recurrent networks architectures State-space Model Learning algorithms Neuro-Fuzzy Systems Preliminary of Fuzzy logic systems Realization of fuzzy logic systems into connectionist networks Existing neuro-fuzzy systems Existing Systematic/Unified Learning Frameworks
參考教材	Simon Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, 1999. C.T. Lin and C.S.G. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems, Prentic-Hall PTR, 1996. IEEE Transactions on Neural Networks Proceedings of IEEE International Conference on Neural Networks
講授方式	講授及課堂討論
成績考評	1. 課程書面報告 (50%) 2. 期刊評論 (25%) 3. 課堂參與 (10%) 4. 期刊上台報告 (15%)
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