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-MuEnd date: 1999

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Now showing 1 - 10 of 23
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    On-Learning Neurocontrol of Nonlinear Systems
    (1994-12-01) Yeh, Zong-Mu
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    A self-organizing decentralized fuzzy neural net controller
    (Institute of Electrical and Electronics Engineers (IEEE), 1995-03-20) Yeh, Zong-Mu; Chen, Hung-Pin
    This paper presents a self-organizing decentralized learning controller using fuzzy control and neurocontrol for large-scale nonlinear systems. A new online unsupervised learning method which is based on a performance index of sliding mode control is used to train the fuzzy neural net controller to obtain control actions. To overcome the interactions between the subsystems, a learning algorithm is adopted to modify the control input to improve the system performance. The effectiveness and the performance of the proposed approach are illustrated by the simulation results of a two-inverted pendulum system and a two-link manipulator. The attractive features also include a smaller residual error and robustness against nonlinear interactions.
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    A Decentralized Learning controller for Nonlinear Systems
    (Institute of Electrical and Electronics Engineers (IEEE), 1994-12-05) Yeh, Zong-Mu; Hsu, How-Gao; Lien, Ching-Chieh
    This paper presents a decentralized learning controller for large-scale nonlinear systems. A new learning method which is based on a performance index of sliding mode control is used to derive an iterative learning algorithm to obtain control actions. The effectiveness and the performance of the proposed approach are illustrated by the simulation results of a two-inverted pendulum system and a two-link manipulator. The attractive features also include a smaller residual error and robustness against nonlinear interactions.
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    A Decentralized fuzzy logic controller Design
    (Institute of Electrical and Electronics Engineers (IEEE), 1994-06-26) Yeh, Zong-Mu
    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 eliminate 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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    A Self-Organized Neural Fuzzy Logic controller
    (1994-12-01) Yeh, Zong-Mu; Tarng, Y.S.; Nian, C.Y.
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    Adaptive Fuzzy Logic controller
    (1995-12-07) Yeh, Zong-Mu
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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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    An Adaptive Neural Net controller Design
    (Institute of Electrical and Electronics Engineers (IEEE), 1994-06-27) Yeh, Zong-Mu
    This paper presents a stability method which is based on the stability condition of sliding mode control to derive the learning law for neural net controllers (NNC) to ensure the convergence of the training algorithm and the stability of the closed-loop system. The proposed method is an online approach of a multilayered neural network which does not require any information about the system dynamics, and the lengthy training of the controller can be eliminated by using the proposed approach. The simulation results of a nonlinear 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 of an interconnected system or external disturbances.
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