科技與工程學院

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沿革

科技與工程學院（原名為科技學院）於87學年度成立，其目標除致力於科技與工程教育師資培育外，亦積極培育與科技產業有關之工程及管理專業人才。學院成立之初在原有之工業教育學系、工業科技教育學系、圖文傳播學系等三系下，自91學年度增設「機電科技研究所」，該所於93學年度起設立學士班並更名為「機電科技學系」。本學院於93學年度亦增設「應用電子科技研究所」，並於96學年度合併工教系電機電子組成立「應用電子科技學系」。此外，「工業科技教育學系」於98學年度更名為「科技應用與人力資源發展學系」朝向培育科技產業之人力資源專才。之後，本院為配合本校轉型之規劃，增加學生於科技與工程產業職場的競爭，本院之「機電科技學系」與「應用電子科技學系」逐漸朝工程技術發展，兩系並於103學年度起分別更名為「機電工程學系」及「電機工程學系」。同年，本學院名稱亦由原「科技學院」更名為「科技與工程學院」。至此，本院發展之重點涵蓋教育（技職教育/科技教育/工程教育）、科技及工程等三大領域，並定位為以技術為本位之應用型學院。

107學年度，為配合本校轉型規劃，「光電科技研究所」由原隸屬於理學院改為隸屬本（科技與工程）學院，另增設2學程，分別為「車輛與能源工程學士學位學程」及「光電工程學士學位學程」。

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學院網址：http://www.cot.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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