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- W2351801344 abstract "To solve the model error problem in the Hurst parameter based traditional traffic modeling method brought about by impactive and periodical traffic load,a traffic model for ship electrical power monitoring network based on Lognormal-Gaussian mixture model(GMM) is given.To identify the parameters of this model,an identification method based on variational Bayesian affinity propagation clustering is proposed.In the proposed method,the waveform of the traffic is divided into equitime intervals;then the distribution function of Lognormal traffic parameters in each time interval is identified by variational Bayesian theory that possesses the ability of distributed estimation;finally,using affinity propagation clustering algorithm that can adaptively identify the clustering center,the distribution function is clustered to achieve the mixture parameters for GMM.Experimental results show that the fitting performance of the traffic model based on the proposed method is better than that of the traffic model based on Hurst parameter." @default.
- W2351801344 created "2016-06-24" @default.
- W2351801344 creator A5065296830 @default.
- W2351801344 date "2012-01-01" @default.
- W2351801344 modified "2023-09-23" @default.
- W2351801344 title "A Traffic Modeling Approach for Ship Electrical Power Monitoring Network Based on Lognormal-Gaussian Mixture Model" @default.
- W2351801344 hasPublicationYear "2012" @default.
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