کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
393012 | 665549 | 2013 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A hybrid-forecasting model reducing Gaussian noise based on the Gaussian support vector regression machine and chaotic particle swarm optimization
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: A hybrid-forecasting model reducing Gaussian noise based on the Gaussian support vector regression machine and chaotic particle swarm optimization A hybrid-forecasting model reducing Gaussian noise based on the Gaussian support vector regression machine and chaotic particle swarm optimization](/preview/png/393012.png)
چکیده انگلیسی
In this paper, the relationship between Gaussian noise and the loss function of the support vector regression machine (SVRM) is analyzed, and then a Gaussian loss function proposed to reduce the effect of such noise on the regression estimates. Since the ε-insensitive loss function cannot reduce noise, a novel support vector regression machine, g-SVRM, is proposed, then a chaotic particle swarm optimization (CPSO) algorithm developed to estimate its unknown parameters. Finally, a hybrid-forecasting model combining g-SVRM with the CPSO is proposed to forecast a multi-dimensional time series. The results of two experiments demonstrate the feasibility of this approach.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 238, 20 July 2013, Pages 96–110
Journal: Information Sciences - Volume 238, 20 July 2013, Pages 96–110
نویسندگان
Qi Wu, Rob Law, Edmond Wu, Jinxing Lin,