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Modelling of nonlinear dynamic systems using support vector neurofuzzy networks

Modelling of nonlinear dynamic systems using support vector ...

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第十九届中国控制会议

作者： K．C．Cheung C．J．Harris Department of Mechanical Engineering The University of Hong Kong Pokfulam Road Hong KongChina Department of Electronics and Computing University of Southampton UK

＜正＞Though neurofuzzy networks can approximate a nonlinear system with arbitrary accuracy, good generalization results are obtained only if the structure of the network is suitably chosen. It is therefore important that there are reliable techniques to select the ’best’ structure of the network. The Support Vector neurofuzzy network （SVNFN） derived here from the Support Vector Regression （SVR） has the special feature that its structure is selected for the error bound specified by the user, similar to that of the SVR. As the output of the network is linear in its weights, the weights can be estimated by the linear least squares method. Important properties of the SVNFNs, such as bounded modelling error and unbiased estimate of the dynamics of the system are also derived. To illustrate the performance of the SVNFN, it is applied to model a nonlinear system.

关键词： neurofuzzy network B-spline kernel Support Vector Regression least squares method.

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Fault Diagnosis Based on Knowledge Extracted from neurofuzzy networks Using Binary and Real-Valued Fault Databases

Fault Diagnosis Based on Knowledge Extracted from Neurofuzzy...

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第二十七届中国控制会议

作者： Mok Hingtung Department of Mechanical Engineering The University of Hong Kong

In this paper,an online fault diagnosis scheme for nonlinear systems is derived from fuzzy rules extracted from the neurofuzzy network that models the residuals of the *** the neurofuzzy network is updated online by the recursive least squares method,the proposed technique is able to diagnose faults *** initiate the fault diagnosis scheme,a binary or real-valued fault database is constructed first from fuzzy rules extracted from each of the neurofuzzy networks that model the possible faults in the *** are diagnosed online by comparing the currently extracted fuzzy rules with those in the fault database using a *** an illustration,a nonlinear DC motor control system is used to illustrate the implementation of the proposed diagnosis schemes,and the performance of the binary and real-valued fault databases is compared.

关键词： Fault Diagnosis DC Motor neurofuzzy network Fault Classifier

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