電機工程學系

Permanent URI for this communityhttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/85

歷史沿革

本系成立宗旨在整合電子、電機、資訊、控制等多學門之工程技術,以培養跨領域具系統整合能力之電機電子科技人才為目標,同時配合產業界需求、支援國家重點科技發展,以「系統晶片」、「多媒體與通訊」、與「智慧型控制與機器人」等三大領域為核心發展方向,期望藉由學術創新引領產業發展,全力培養能直接投入電機電子產業之高級技術人才,厚植本國科技產業之競爭實力。

本系肇始於民國92年籌設之「應用電子科技研究所」,經一年籌劃,於民國93年8月正式成立,開始招收碩士班研究生,以培養具備理論、實務能力之高階電機電子科技人才為目標。民國96年8月「應用電子科技學系」成立,招收學士班學生,同時間,系所合一為「應用電子科技學系」。民國103年8月更名為「電機工程學系」,民國107年電機工程學系博士班成立,完備從大學部到博士班之學制規模,進一步擴展與深化本系的教學與研究能量。

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    A dynamic hierarchical fuzzy neural network for a general continuous function
    (2008-06-06) W.-Y. Wang; I-H. Li; S.-C. Li; M.-S. Tsai; S.-F. Su
    A serious problem limiting the applicability of the fuzzy neural networks is the "curse of dimensionality", especially for general continuous functions. A way to deal with this problem is to construct a dynamic hierarchical fuzzy neural network. In this paper, we propose a two-stage genetic algorithm to intelligently construct the dynamic hierarchical fuzzy neural network (HFNN) based on the merged-FNN for general continuous functions. First, we use a genetic algorithm which is popular for flowshop scheduling problems (GAFSP) to construct the HFNN. Then, a reduced-form genetic algorithm (RGA) optimizes the HFNN constructed by GAFSP. For a real-world application, the presented method is used to approximate the Taiwanese stock market.
  • Item
    A Dynamic Hierarchical Fuzzy Neural Network for A General Continuous Function
    (中華民國模糊學會, 2009-06-01) W.-Y. Wang; I-H. Li; S.-C. Li; M.-S. Tsai; S.-F. Su
    A serious problem limiting the applicability of the fuzzy neural networks is the “curse of dimensionality”, especially for general continuous functions. A way to deal with this problem is to construct a dynamic hierarchical fuzzy neural network. In this paper, we propose a two-stage genetic algorithm to intelligently construct the dynamic hierarchical fuzzy neural network (HFNN) based on the merged-FNN for general continuous functions. First, we use a genetic algorithm which is popular for flowshop scheduling problems (GA_FSP) to construct the HFNN. Then, a reduced-form genetic algorithm (RGA) optimizes the HFNN constructed by GA_FSP. For a real-world application, the presented method is used to approximate the Taiwanese stock market.