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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系所網址：https://www.ee.ntnu.edu.tw/

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Author: search.filters.author.Chen-Chien HsuSubject: search.filters.subject.genetic algorithms

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    Discrete Modelling of Continuous-Time Systems Having Interval Uncertainties Using Genetic Algorithms
    (Institute of Electronics, Information and Communication Engineers, 2008-01-01) Chen-Chien Hsu; Tsung-Chi Lu; Heng-Chou Chen
    In this paper, an evolutionary approach is proposed to obtain a discrete-time state-space interval model for uncertain continuous-time systems having interval uncertainties. Based on a worst-case analysis, the problem to derive the discrete interval model is first formulated as multiple mono-objective optimization problems for matrix-value functions associated with the discrete system matrices, and subsequently optimized via a proposed genetic algorithm (GA) to obtain the lower and upper bounds of the entries in the system matrices. To show the effectiveness of the proposed approach, roots clustering of the characteristic equation of the obtained discrete interval model is illustrated for comparison with those obtained via existing methods.
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    Multiobjective Evolutionary Approach to the Design of Optimal Controllers for Interval Plants Based on Parallel Computation
    (Institute of Electronics, Information and Communication Engineers, 2006-09-01) Chen-Chien Hsu; Shih-Chi Chang; Chih-Yung Yu
    Design of optimal controllers satisfying performance criteria of minimum tracking error and disturbance level for an interval system using a multi-objective evolutionary approach is proposed in this paper. Based on a worst-case design philosophy, the design problem is formulated as a minimax optimization problem, subsequently solved by a proposed two-phase multi-objective genetic algorithm (MOGA). By using two sets of interactive genetic algorithms where the first one determines the maximum (worst-case) cost function values for a given set of controller parameters while the other one minimizes the maximum cost function values passed from the first genetic algorithm, the proposed approach evolutionarily derives the optimal controllers for the interval system. To suitably assess chromosomes for their fitness in a population, root locations of the 32 generalized Kharitonov polynomials will be used to establish a constraints handling mechanism, based on which a fitness function can be constructed for effective evaluation of the chromosomes. Because of the time-consuming process that genetic algorithms generally exhibit, particularly the problem nature of minimax optimization, a parallel computation scheme for the evolutionary approach in the MATLAB-based working environment is also proposed to accelerate the design process.�