教師著作

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    ESCSD-Expert system for control system design
    ( 中國工程師學會, 1992-07-01) C.-H. Wang; W.-Y. Wang
    The purpose of this paper is to design an expert system for control system design. The architecture of ESCSD is designed and implemented using CLIPS, which is an expertsystem building tool. The achievements of ESCSD are extracting the heuristics ofdesign approaches, building design methods into knowledge‐bases, partitioning of knowledge‐bases, and providing explanation facilities. The user interface of ESCSD is icon‐based with pop‐up menus for user selections. We have demonstrated in this paper that this kind of user interface is better than previous similar systems, where complex dialogues are required. Also, due to the flexible partitions of the knowledge‐bases, ESCSD can be implemented successfully on the IBM PC with a limited 640K‐byte MSDOS environment. It is further explained that, regardless of the computer size, the knowledge‐bases must be partitioned into the smallest entities to allow future expansion. Several design examples are fully illustrated to clarify the advantages of using the expert system to design control systems.
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    On constructing fuzzy membership functions and applications in fuzzy neural networks
    (1993-10-29) C.-H. Wang; T.-T. Lee; W.-Y. Wang; P.-S. Tseng
    A unified form of fuzzy membership functions, called as B-spline membership functions (BMFs) is proposed. The computer simulation of fuzzy control of a model car is considered as an application of BMFs in fuzzy neural networks. The example shows that the number of iterations for learning is substantially less than that of conventional methods.
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    Sampling-time effects of higher-order digitisations and their applications in digital redesign
    (IET, 1994-03-01) C.-H. Wang; W.-Y. Wang; T.-T. Lee
    A study is made of the sampling-time effects of higher-order digitisations (i.e. the Madwed and Boxer-Thaler digitisations) to convert a continuous-time system into a discrete-time system. A general expression for the denominator and numerator of the digitised system is proposed, and used to predict precisely the computational stability and sampling-time effects of these types of digitisation. The 'polynomial root locus' is introduced to describe the pole variations of the digitised system when the sampling time is varied from zero to infinity. The maximum sampling time of a particular digitisation can also be found by a new algorithm which is proposed. The transient behaviour of the digitised system is further studied by defining a new set of transient terms for discrete-time systems. In this way, the effects of sampling-time can be studied thoroughly. It is shown that the appropriate sampling times obtained via these approximate methods play a meaningful role in selecting appropriate sampling times for real problems. Several examples are illustrated.
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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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    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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    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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    Fuzzy B-spline membership function (BMF) and its applications in fuzzy-neural control
    (IEEE Systems, Man, and Cybernetics Society, 1995-05-01) C.-H. Wang; W.-Y. Wang; T.-T. Lee; P.-S. Tseng
    A general methodology for constructing fuzzy membership functions via B-spline curves is proposed. By using the method of least-squares, the authors 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 a B-spline membership function (BMF). By using the local control property of a B-spline curve, the BMFs can be tuned locally during the learning process. For the control of a model car through fuzzy-neural networks, it is shown that the local tuning of BMFs can indeed reduce the number of iterations tremendously. This fuzzy-neural control of a model car is presented to illustrate the performance and applicability of the proposed method
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    Impact of sampling time on tustin digitization
    (ACTA Press, 1996-01-01) C.-H. Wang; W.-Y. Wang; C.-C. Hsu
    This paper investigates the impact of sampling time on Tustin digitization. A Q-matrix representation for the digitized system via Tustin transformation is first proposed. It is shown that Tustin transformation is a special case of the higher-order integrator approaches to digitize a continuous system. Pole-variation loci is then introduced to describe the trajectories of poles of the digitized system using Tustin transformation when sampling time is varied from zero to infinity. With new theorems derived in this paper, the pole-variation loci can be easily sketched. Sampling time of any point on the pole-variation loci of the digitized system can be determined by the angle of the vector drawn from the origin to the designated pole location. System dynamics of the digitized system can then be estimated from the sampling time, which determines the pole locations.
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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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    Minimum-phase criteria for sampled systems via symbolic approach
    (Taylor & Francis, 1997-01-01) 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 proposed approach in this note has less restrictions on the continuous plant and is very easy to implement in any symbolic manipulation package. Several examples are illustrated to show the effectiveness of this approach.
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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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    Function approximation using fuzzy neural networks with robust learning algorithm
    (IEEE Systems, Man, and Cybernetics Society, 1997-08-01) W.-Y. Wang; T.-T. Lee; C.-L. Liu; C.-H. Wang
    The paper describes a novel application of the B-spline membership functions (BMF's) and the fuzzy neural network to the function approximation with outliers in training data. According to the robust objective function, we use gradient descent method to derive the new learning rules of the weighting values and BMF's of the fuzzy neural network for robust function approximation. In this paper, the robust learning algorithm is derived. During the learning process, the robust objective function comes into effect and the approximated function will gradually be unaffected by the erroneous training data. As a result, the robust function approximation can rapidly converge to the desired tolerable error scope. In other words, the learning iterations will decrease greatly. We realize the function approximation not only in one dimension (curves), but also in two dimension (surfaces). Several examples are simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper
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    On-line tuning of fuzzy-neural network for adaptive control of nonlinear dynamical systems
    (IEEE Systems, Man, and Cybernetics Society, 1997-12-01) Y.-G. Leu; T.-T. Lee; W.-Y. Wang
    The adaptive fuzzy-neural controllers tuned online for a class of unknown nonlinear dynamical systems are proposed. To approximate the unknown nonlinear dynamical systems, the fuzzy-neural approximator is established. Furthermore, the control law and update law to tune on-line both the B-spline membership functions and the weighting factors of the adaptive fuzzy-neural controller are derived. Therefore, the control performance of the controller is improved. Several examples are simulated in order to confirm the effectiveness and applicability of the proposed methods in this paper
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    Approximationransform using higher order integrators and its applications in sampled-data control systems
    (Taylor & Francis, 1998-01-01) C.-H. Wang; C.-C. Hsu; W.-Y. Wang
    In this paper, we first clarify the difference between the approximate z transform and the discrete equivalent of a continuous system using higher-order integrators. It is shown that a 1/ ts factor needs to be included for the approximate z transform but not for the discrete equivalent. We further apply the approximate z transform to facilitate the stability analysis of sampled-data control systems, with or without uncertain parameters, ft is shown in this paper that the approximate z transform greatly simplifies the stability analysis of a sampled-data control system, which is regarded as rather difficult ( if not impossible) to handle because of its transcendental nature. The results can be easily obtained and show reasonably good approximations with this approach. Several examples are used to illustrate the effectiveness of this new method.
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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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    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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    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
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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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    Observer-based adaptive fuzzy-neural control for unknown nonlinear dynamical systems
    (IEEE Systems, Man, and Cybernetics Society, 1999-10-01) Y.-G. Leu; T.-T. Lee; W.-Y. Wang
    In this paper, an observer-based adaptive fuzzy-neural controller for a class of unknown nonlinear dynamical systems is developed. The observer-based output feedback control law and update law to tune on-line the weighting factors of the adaptive fuzzy-neural controller are derived. The total states of the nonlinear system are not assumed to be available for measurement. Also, the unknown nonlinearities of the nonlinear dynamical systems are not restricted to the system output only. The overall adaptive scheme guarantees that all signals involved are bounded. Simulation results demonstrate the applicability of the proposed method in order to achieve desired performance
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    Robust adaptive fuzzy-neural controllers for uncertain nonlinear systems
    (IEEE Robotics and Automation Society, 1999-10-01) Y.-G. Leu; W.-Y. Wang; T.-T. Lee
    A robust adaptive fuzzy-neural controller for a class of unknown nonlinear dynamic systems with external disturbances is proposed. The fuzzy-neural approximator is established to approximate an unknown nonlinear dynamic system in a linearized way. The fuzzy B-spline membership function (BMF) which possesses a fixed number of control points is developed for online tuning. The concept of tuning the adjustable vectors, which include membership functions and weighting factors, is described to derive the update laws of the robust adaptive fuzzy-neural controller. Furthermore, the effect of all the unmodeled dynamics, BMF modeling errors and external disturbances on the tracking error is attenuated by the error compensator which is also constructed by fuzzy-neural inference. We prove that the closed-loop system which is controlled by the robust adaptive fuzzy-neural controller is stable and the tracking error will converge to zero under mild assumptions. Several examples are simulated in order to confirm the effectiveness and applicability of the proposed methods