教師著作

Permanent URI for this collectionhttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/31268

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Start date: 1992End date: 1999

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Now showing 1 - 10 of 22
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    Intel 8088 80X86 系列微處理器架構：規畫與介面
    (東華書局, 1995-01-01) 曹恆偉; 郭建宏; 陳建中譯; BREY
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    微電子學
    (台北圖書, 1999-01-01) 曹恆偉; 林浩雄; 郭建宏; 陳建中譯; Sedra and Smith
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    以信號能量相似為基礎之數位化再設計系統性能的評估
    (行政院國家科學委員會, 1999-07-31) 許陳鑑
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    適應性模糊類神經控制器線上調及強健性學習法則之研究
    (行政院國家科學委員會, 1997-07-31) 王偉彥
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    以DSP基礎建立即時模糊類神經網路之研究
    (行政院國家科學委員會, 1998-07-31) 王偉彥
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    Minimum-phase criteria for sampled systems via symbolic approach
    (1996-12-13) C.-H. Wang; W.-Y. Wang; C.-C. Hsu
    In this paper, we propose a symbolic approach to determine the sampling-time range which guarantees minimum-phase behaviours for a sampled system with a zero-order hold. By using Maple, a symbolic manipulation package, the symbolic transfer function of the sampled system, which contains sampling time T as an independent variable, can be easily obtained. We then adopt the critical stability constraints to determine the sampling-time range which ensures that the sampled system has only stable zeros. In comparison with existing methods, the approach proposed in this paper has less restrictions on the continuous plant and is very easy to implement in any symbolic manipulation packages. Several examples are illustrated to show the effectiveness of this approach
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    DSP-based fuzzy neural networks and its application in speech recognition
    (1999-10-15) S.-C. Chen; C.-C. Hsu; W.-Y. Wang
    A fuzzy-neural network needs to be trained through a learning process, so that suitable membership functions and weightings can be obtained. However, most neural networks are only simulated by computer software, which are not practical for real applications. It is therefore our objective to design an integrated circuit system based on a DSP processor with powerful arithmetical capabilities and fast data processing, and relevant peripheral devices to implement the fuzzy neural network. In terms of implementation cost and feasibility for practical applications, this DSP-based fuzzy neural network will be more practical and usable. Finally, a prospective application of the DSP processor-based fuzzy neural network to recognize speech from a non-designated person is proposed
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    Fuzzy B-spline membership function (BMF) and its applications in fuzzy-neural control
    (1994-10-05) C.-H. Wang; W.-Y. Wang
    A general methodology for constructing fuzzy membership functions via B-spline curve is proposed. By using the method of least-squares, we translate the empirical data into the form of the control points of B-spline curves to construct fuzzy membership functions. This unified form of fuzzy membership functions is called as B-spline membership functions (BMF's). By using the local control property of B-spline curve, the BMF's can be tuned locally during learning process. For the control of a model car through fuzzy-neural networks, it is shown that the local tuning of BMF's can indeed reduce the number of iterations tremendously
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    Fuzzy evaluation and expert system in classical control system design
    (1994-07-01) C.-H. Wang; W.-Y. Wang; T.-T. Lee
    The purpose of this paper is to develop an expert system for control system design (ESCSD), with a unique set of fuzzy evaluation rules. The authors' investigation not only uses expert systems for control system design but also proposes a practical way to use a unique set of fuzzy evaluation rules to suggest a better design method for a given plant. A set of fuzzy evaluation rules extracted from four classical design procedures is proposed. It focuses on how to predict the results of design methods. The authors deem the fuzzy evaluation rules are predicting tools of an expert system. It is also shown in this paper that the set of fuzzy evaluation rules has been successfully integrated with ESCSD. Several examples are illustrated which show the agreeable result obtained from ESCSD.
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    Discrete modeling of continuous interval using high-order integrators
    (1999-06-04) C.-C. Hsu; W.-Y. Wang
    A higher-order integrator approach is proposed to obtain an approximate discrete-time transfer function for uncertain continuous systems having interval uncertainties. Thanks to simple algebraic operations of this approach, the resulting discrete model is a rational function of the uncertain parameters. The problem of non-linearly coupled coefficients of exponential nature in the exact discrete-time transfer function is therefore circumvented. Furthermore, interval structure of the uncertain continuous-time system is preserved in the resulting discrete model by using this approach. Formulas to obtain the lower and upper bounds for the discrete interval system are derived, so that existing robust results in the discrete-time domain can be easily applied to the discretized system. Digital simulation and design for the continuous-time interval plant can then be performed based on the obtained discrete-time interval model