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.I-H. Li

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Now showing 1 - 10 of 22
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    A merged-fuzzy-neural network and its application in fuzzy-neural control
    (2006-10-11) I-H. Li; W.-Y. Wang; S.-F. Su; M.-C. Chen
    This paper proposes an observer-based adaptive fuzzy-neural controller, structured by a merged fuzzy-neural network (merged-FNN) to reduce the number of adjustable parameters. In this paper, the merged-FNN is proved to take the place of the traditional fuzzy-neural networks under some assumptions. Moreover, the overall adaptive schemes using the proposed merged-FNN guarantees that all signals involved are bounded and the output of the closed-loop system asymptotically tracks the desired output trajectory. From experimental examples, the proposed merged-FNN has far fewer parameters than the traditional FNN, and the computation time is significantly reduced. To demonstrate the effectiveness of the proposed methods, simulation results are illustrated in this paper.
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    T-S fuzzy control for uncertain nonlinear systems using adaptive fuzzy approach
    (2006-07-21) L.-H. Chien; W.-Y. Wang; I-H. Li; S.-F. Su
    This paper proposes on-line modeling via Takagi-Sugeno (T-S) fuzzy models and robust adaptive control for a class of unknown nonlinear dynamic systems with external disturbances. The T-S fuzzy model is established to approximate an unknown nonlinear dynamic system in a linearized way. Fuzzy B-spline membership functions (BMFs) which possesses a fixed number of control points are developed for on-line tuning. In this paper, the closed-loop system which is controlled by the proposed controller can be stabilized and the tracking error will converge to zero. An example is simulated in order to confirm the effectiveness and applicability of the proposed methods in this paper.
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    A new convergence condition for discrete-time nonlinear system identification using a hopfield neural network
    (2005-10-12) W.-Y. Wang; I-H. Li; W.-M. Wang; S.-F. Su; N.-J. Wang
    This paper presents a method of discrete time nonlinear system identification using a HopfieId neural network (HNN) as a coefficient learning mechanism to obtain optimized coefficients over a set of Gaussian basis functions. A linear combination of Gaussian basis functions is used to replace the nonlinear function of the equivalent discrete time nonlinear system. The outputs of the HNN, which are coefficients over a set of Gaussian basis functions, are discretized to be a discrete Hopfield learning model. Using the outputs of the HNN, one can obtain the optimized coefficients of the linear combination of Gaussian basis functions conditional on properly choosing an activation function scaling factor of the HNN. The main contributions of this paper is that the convergence of learning of the HNN can be guaranteed if the activation function scaling factor is properly chosen. Finally, to demonstrate the effectiveness of the proposed methods, simulation results are illustrated in this paper.
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    Intelligent Control for Anti-lock Braking Systems to Track Dynamic Optimal Slip Ratio
    (2007-01-01) W.-Y. Wang; I-H. Li; D.-F. Chung; M.-C. Chen
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    MIMO Robust Control via T-S Fuzzy Models for Nonaffine Nonlinear Systems
    (2007-07-26) W.-Y. Wang; L.-C. Chien; I-H. Li; S.-F. Su
    This paper proposes on-line modeling via Takagi-Sugeno (T-S) fuzzy models and robust adaptive control for a class of generalized multiple input multiple output (MIMO) nonlinear dynamic systems with external disturbances. The T-S fuzzy model is established to approximate the nonaffine nonlinear dynamic system in a linearized way and is used to be an error compensator for external disturbances and system uncertainly, i.e. the unmodeled dynamics, modeling errors and external disturbances. In second type adaptive laws, fuzzy B-spline membership functions (BMFs) are developed for on-line tuning. In this paper, we can prove that the closed-loop system which is controlled by the proposed controller is stable and the tracking error will converge to zero.
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    MIMO Robust Control via Adaptive T-S Merged Fuzzy Models for Nonaffine Nonlinear Systems
    (2007-01-01) W.-Y. Wang; Y.-H. Chien; I-H. Li; T.-T. Lee
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    Dynamic Slip Ratio Estimation and Control of Antilock Braking Systems Considering Wheel Angular Velocity
    (2007-10-10) M.-C. Chen; W.-Y. Wang; I-H. Li; S.-F. Su
    This paper proposes an antilock braking system (ABS), in which unknown road characteristics are resolved by a road estimator. This estimator is based on the LuGre friction model with a road condition parameter, and can transmit a reference slip ratio to a slip ratio controller through a mapping function considering the effect of wheel angular velocity. In the controller design, a direct adaptive fuzzy-neural controller (DAFC) for an ABS is developed. Finally, this paper gives simulation results of an ABS with the road estimator and the DAFC, and shows good effectiveness under varying road conditions.
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    Adaptive T-S fuzzy controller for a class of general nonlinear systems
    (2006-01-01) W.-Y. Wang; Y.-H. Chien; I-H. Li
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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.
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