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Expert Systems with Applications: An International Journal
A Fuzzy Support Vector Machine with Weighted Margin for Flight Delay Early Warning
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Prediction of flashover voltage of insulators using least squares support vector machines
Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fuzzy classifier based on fuzzy support vector machine
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In view of the shortage of @e-insensitive loss function for hybrid noises such as singularity points, biggish magnitude noises and Gaussian noises, this paper presents a new version of fuzzy support vector machine (SVM) which can penalize those hybrid noises to forecast fuzzy nonlinear system. Since there exist some problems of hybrid noises and uncertain data in many actual forecasting problem, the input variables are described as fuzzy numbers by fuzzy comprehensive evaluation. Then by the integration of the triangular fuzzy theory, @n-SVM and loss function theory, the fuzzy robust @n-SVM with robust loss function (FR@n-SVM) which can penalize those hybrid noises is proposed. To seek the optimal parameters of FR@n-SVM, particle swarm optimization is also proposed to optimize the unknown parameters of FR@n-SVM. The results of the application in fuzzy sale system forecasts confirm the feasibility and the validity of the FR@n-SVM model. Compared with the traditional model and other SVM methods, FR@n-SVM method requires fewer samples and has better generalization capability for Gaussian noise.