دانلود مقاله ISI انگلیسی شماره 25908
عنوان فارسی مقاله

انعطاف پذیر رگرسیون بردار پشتیبانی و کاربرد آن در تشخیص خطا

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
25908 2013 13 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Flexible Support Vector Regression and Its Application to Fault Detection
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Acta Automatica Sinica, Volume 39, Issue 3, March 2013, Pages 272–284

کلمات کلیدی
() رگرسیون بردار پشتیبانی - انعطاف پذیر - عیب یابی - منبع تغذیه
پیش نمایش مقاله
پیش نمایش مقاله انعطاف پذیر رگرسیون بردار پشتیبانی و کاربرد آن در تشخیص خطا

چکیده انگلیسی

Hyper-parameters, which determine the ability of learning and generalization for support vector regression (SVR), are usually fixed during training. Thus when SVR is applied to complex system modeling, this parameters-fixed strategy leaves the SVR in a dilemma of selecting rigorous or slack parameters due to complicated distributions of sample dataset. Therefore in this paper we proposed a flexible support vector regression (F-SVR) in which parameters are adaptive to sample dataset distributions during training. The method F-SVR divides the training sample dataset into several domains according to the distribution complexity, and generates a different parameter set for each domain. The efficacy of the proposed method is validated on an artificial dataset, where F-SVR yields better generalization ability than conventional SVR methods while maintaining good learning ability. Finally, we also apply F-SVR successfully to practical fault detection of a high frequency power supply.

نتیجه گیری انگلیسی

The parameter setting strategy for SVR has been stud-ied for a decade, however there is still no general consensus on theoretical estimation. This enhances the di±culty for ordinary users in applying SVR to practical applications.Moreover, as investigated in Section 1, the conventional SVR approaches are also in a dilemma of selecting rigorous or slack parameters for training some complicated distribu-tions. Hence, a °exible support vector regression (F-SVR) approach has been proposed in this paper, which is capa-ble of adapting the parameters to the sample distribution instead using of ¯xed parameters.With automatic parameter setting, the learning ability is maintained in the F-SVR whilst the number of support vec-tors is also reduced. The proposed approach o®ers a better trade-o® between over-¯tting and under-¯tting. The e®ec- tiveness of the F-SVR is validated by applying it to an arti-¯cial dataset, where appropriate hyper-parameters are hard to be set due to its complicated sample distribution. During the experiment, the F-SVR succeeded in making a regres-sion with su±cient learning and better generalization abil-ity, and showed its superiority to conventional approaches.We also made an attempt of employing the approach to de- tect the leaning excitation fault for high frequency power supply. The F-SVR o®ered a good description of the rela- tionship between the SPC and operating status, and showed its applicability in practice. However, while the setting of the three parameters is avoided, a new parameter for sample division \k" is aroused in this approach. This parameter may greatly a®ect the computational cost during training, and requires further study in the future.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.