教師著作

Permanent URI for this collectionhttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/31266

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Author: search.filters.author.Yeh, Zong-MuSubject: search.filters.subject.Fuzzy control

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Now showing 1 - 6 of 6
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    Multi-stage Inference Fuzzy Logic Control
    (Institute of Electrical and Electronics Engineers (IEEE), 1997-07-01) Yeh, Zong-Mu; Chen, Hung-Pin
    This paper presents a methodology to the design of a multistage inference fuzzy controller in which the consequence in an inference stage is passed to the next stage as fact, and so forth. A new general method which is based on a performance index of the control system is used to generate fuzzy rule bases for the multistage inference. This proposed method can reduce the design cycle time. The new method has been applied to a two-trailer-and-truck system. The simulation studies showed that proposed method is feasible.
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    A Bi-tree Multi-stage Inference Fuzzy Control System
    (Institute of Electrical and Electronics Engineers (IEEE), 1996-12-11) Yeh, Zong-Mu; Chen, Hung-Pin
    This paper presents a methodology for the design of a binary tree multi-stage inference fuzzy controller in which the consequence in an inference stage is passed to the next stage as fact, and so forth. A new general method which is based on a performance index of the control system is used to generate fuzzy rule bases for bi-tree multi-stage inference. This proposed method can be used to reduce the complexity of fuzzy rule sets. The new method has been applied to control a truck-and-two-trailer system. The simulation studies showed that the proposed method is feasible.
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    A Systematic Approach for Design of Multistage Fuzzy Control Systems
    (Elsevier, 2004-04-16) Yeh, Zong-Mu; Li, Kuei-Hsiang
    This paper proposes a systematic approach for designing a multistage fuzzy logic controller (MFLC) for large scale nonlinear systems. In designing such a controller, the major tasks are to derive fuzzy rule bases, determine membership functions of input/output variables, and design input/output scaling factors. In this work, the fuzzy rule bases are generated by rule-generated functions which are based on the negative gradient of a system performance index. The membership functions of isosceles triangle of input/output variables are fixed in the same cardinality, and only the input/output scaling factors are optimized from a genetic algorithm based on a fitness function. As a result, the search space of the parameters is narrowed down to a small space so that the MFLC can be quickly constructed and the fuzzy rules and scaling factors can easily be determined. The performance of the proposed approach is examined by computer simulations on an inverted pendulum system. The performance of single stage structure, binary tree structure and skew-binary tree structure are compared. The binary tree structure has better performance and use fewer fuzzy rules in the illustrative example.
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    A Novel Approach for Multi-Stage Inference Fuzzy Control
    (Institute of Electrical and Electronics Engineers (IEEE), 1998-12-01) Yeh, Zong-Mu; Chen, Hung-Pin
    This paper presents a methodology to the design of a multistage inference fuzzy controller in which the consequence in an inference stage is passed to the next stage as fact, and so forth. A new general method which is based on a performance index of the control system is used to generate fuzzy rule bases for the multistage inference. This proposed method can reduce the design cycle time. In order to reduce the computation time, a method for precomputing the match-degrees of fuzzy values is adopted. Thus, the number of operations that must be carried out at execution time can be significantly reduced. The new method has been applied to two applications, a two-trailer-and-truck system and a three-trailer-and-truck system. The simulation studies showed that the proposed method is feasible.
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    A performance approach to fuzzy control design for nonlinear systems
    (Elsevier, 1994-06-24) Yeh, Zong-Mu
    This paper presents a systematic methodology to the design of a decentralized fuzzy logic controller for large-scale nonlinear systems. A new method which is based on a performance index of sliding mode control is used to derive fuzzy rules and an adaptive algorithm is used to reduce the chattering phenomenon. The simulation results of a two-inverted pendulum system and a two-link manipulator demonstrate that the attractive features of the proposed approach include a smaller residual error and robustness against nonlinear interactions.
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    Adaptive Multivariable Fuzzy Logic controller
    (Elsevier, 1997-02-16) Yeh, Zong-Mu
    This paper presents a systematic methodology to the design of a multivariable fuzzy logic controller (MFLC) for large-scale nonlinear systems. A new general method which is based on a performance index of sliding motion is used to generate a fuzzy control rule base. Reducible input variables obtained from sliding motion are adopted as input variable of the fuzzy controller and the output scale factors of the MFLC are tuned by the switching variable. Thus, the determination of the input/output scale factors becomes easier and the system performance is significantly improved. The simulation results of a Puma 560 system and a two-inverted pendulum system demonstrate that the attractive features of this proposed approach include a smaller residual error and robustness against nonlinear interactions.