Hybrid genetic algorithms and support vector regression in forecasting atmospheric corrosion of metallic materials

Fang, Shuangkang; Wang, M.P.; Wang, Qi; Zheng, Fang

doi:10.1016/j.commatsci.2008.05.010

Cited by 89 publications

(33 citation statements)

References 25 publications

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“…The principle of ANN adopted in generation is empirical risk minimization (ERM) (Fang et al, 2008). ERM minimizes the error on the training data, and it vulnerably suffers problem of producing models that can over-fit the data in generalization.…”

Section: Discussion Of Discrimination Resultsmentioning

confidence: 99%

Identification of egg’s freshness using NIR and support vector data description

Zhao

Hao

Chen

et al. 2010

Journal of Food Engineering

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Section: Discussion Of Discrimination Resultsmentioning

confidence: 99%

Identification of egg’s freshness using NIR and support vector data description

Zhao

Hao

Chen

et al. 2010

Journal of Food Engineering

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“…As to the investigation between ANN and SVM models, the traditional ANN models are based on the empirical risk minimisation (ERM) principle (Fang et al 2008). ERM minimises the error of the training data, suffering from the problem of producing models that can over-fit the data in generalisation.…”

Section: Discussion Of Discrimination Resultsmentioning

confidence: 99%

Eggshell crack detection based on acoustic impulse response and supervised pattern recognition

Hao¹,

Zhao²,

Chen³

et al. 2009

Czech J. Food Sci.

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Lin H., Zhao J., Chen Q., Cai J., Zhou P. (2009): Eggshell crack detection based on acoustic impulse response and supervised pattern recognition. Czech J. Food Sci., 27: 393-402.A system based on acoustic resonance was developed for eggshell crack detection. It was achieved by the analysis of the measured frequency response of eggshell excited with a light mechanism. The response signal was processed by recursive least squares adaptive filter, which resulted in the signal-to-noise ratio of the acoustic impulse response reing remarkably enhanced. Five features variables were exacted from the response frequency signals. To develop a robust discrimination model, three pattern recognition algorithms (i.e. K-nearest neighbours, artificial neural network, and support vector machine) were examined comparatively in this work. Some parameters of the model were optimised by cross-validation in the building model. The experimental results showed that the performance of the support vector machine model is the best in comparison to k-nearest neighbours and artificial neural network models. The optimal support vector machine model was obtained with the identification rates of 95.1% in the calibration set, and 97.1% in the prediction set, respectively. Based on the results, it was concluded that the acoustic resonance system combined with the supervised pattern recognition has a significant potential for the cracked eggs detection.

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“…In order to present our approach clearly, we first make a brief review of e-SVM (Fang et al, 2008). Let the training…”

Section: The Tf-e-svr Approachmentioning

confidence: 99%

A trend fixed on firstly and seasonal adjustment model combined with the ε-SVR for short-term forecasting of electricity demand

et al. 2009

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Hybrid genetic algorithms and support vector regression in forecasting atmospheric corrosion of metallic materials

Cited by 89 publications

References 25 publications

Identification of egg’s freshness using NIR and support vector data description

Identification of egg’s freshness using NIR and support vector data description

Eggshell crack detection based on acoustic impulse response and supervised pattern recognition

A trend fixed on firstly and seasonal adjustment model combined with the ε-SVR for short-term forecasting of electricity demand

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