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1998年中国控制会议

作者： K．C．Cheung W．K．Yeung Department of Mechanical Engineering The University of Hong Kong Pokfulam RoadHong Kong Department of Mechanical Engineering The University of Hong Kong Pokfulam RoadHong Kong

＜正＞An on-line training algorithm for neurofuzzy networks with a computation time that is a linear function of the number of weights is presented. Although the proposed algorithm is an approximation to the recursive least squares method, it is shown here that the estimated weights converge to that obtained from the least squares method.

关键词： neurofuzzy networks recursive least squares method convergence of estimated parameters

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Adaptive Control of Nonlinear Systems Using neurofuzzy networks

Adaptive Control of Nonlinear Systems Using Neurofuzzy Netwo...

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第十九届中国控制会议

作者： W．K．Yeung K．C．Cheung Department of Mechanical Engineering The University of Hong Kong Pokfulam Road Hong KongChina Department of Mechanical Engineering The University of Hong Kong Pokfulam Road Hong KongChina

＜正＞An adaptive control scheme for nonlinear systems is proposed. It is assumed that the orders and the delay of the system are known, whilst the nonlinearity is unknown. The controller is designed to minimize the one-step-ahead prediction error of localized linear models, and implemented using neurofuzzy networks. The proposed controller can be trained on line, using a modified recursive least squares method. The stability of the adaptive control scheme is analyzed, and its performance is illustrated by an example.

关键词： Adaptive control nonlinear systems neurofuzzy networks

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An intelligent online fault diagnostic scheme for nonlinear systems

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控制理论与应用（英文版） 2008年第3期6卷 267-272页

作者： Hing Tung MOK Department of Mechanical Engineering The University of Hong Kong

An online fault diagnostic scheme for nonlinear systems based on neurofuzzy networks is proposed in this paper. The scheme involves two stages. In the first stage, the nonlinear system is approximated by a neurofuzzy network, which is trained offline from data obtained during the normal operation of the system. In the second stage, residual is generated online from this network and is modelled by another neurofuzzy network trained online. Fuzzy rules are extracted from this network, and are compared with those in the fault database obtained under different faulty operations, from which faults are diagnosed. The performance of the proposed intelligent fault scheme is illustrated using a two-tank water level control system under different faulty conditions .

关键词： Fault diagnosis Nonlinear systems neurofuzzy networks

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An Intelligent Online Fault Diagnostic Scheme for Nonlinear Systems

An Intelligent Online Fault Diagnostic Scheme for Nonlinear ...

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第25届中国控制会议

作者： Mok H．T. The University of Hong Kong

An online fault diagnostic scheme for nonlinear systems based on neurofuzzy networks is proposed in this paper. The scheme involves two stages. In the first stage, the nonlinear system is approximated by a neurofuzzy network, which is trained offline from data obtained during the normal operation of the system. In the second stage, residual is generated online from this network, which is modelled by another neurofuzzy network trained online. From this network, fuzzy rules can be generated. Comparing these rules with those obtained under different faulty operations, fault can then be diagnosed. The proposed intelligent fault scheme is illustrated using a two-tank water level control system under various faulty conditions.

关键词： Fault diagnosis Nonlinear systems neurofuzzy networks

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