Fuzzy Weighted Least Squares Support Vector Regression with Data Reduction for Nonlinear System Modeling
المؤلفون المشاركون
Ouyang, Aijia
Liu, Xiaoyong
Yun, Zhonghua
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-12-10
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
This paper proposes a fuzzy weighted least squares support vector regression (FW-LSSVR) with data reduction for nonlinear system modeling based only on the measured data.
The proposed method combines the advantages of data reduction with some ideas of fuzzy weighted mechanism.
It not only possesses the capability of illuminating local characteristic of the modeled plant but also can deal with the problem of boundary effects resulted from local LSSVR method when the modeled data is at the boundary of whole data subset.
Furthermore, in comparison of the SVR, the proposed method only utilizes fewer hyperparameters to construct model, and the overlap factor λ can be chosen in relatively smaller value than SVR to further reduce more computational time.
First of all, distilling the original input space into several regions with fuzzy partition by applying Gustafson-Kessel clustering algorithm (GKCA) is a foundation for data reduction and the overlap factor is introduced to reduce the size of subsets.
Following that, those subset regression models (SRMs) which can be simultaneously solved by LSSVR are integrated into an overall output of the estimated nonlinear system by fuzzy weighted.
Finally, the proposed method is demonstrated by experimental analysis and compared with local LSSVR, weighted SVR, and global LSSVR methods by using the index of computational time and root-mean-square error (RMSE).
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Liu, Xiaoyong& Ouyang, Aijia& Yun, Zhonghua. 2018. Fuzzy Weighted Least Squares Support Vector Regression with Data Reduction for Nonlinear System Modeling. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1208844
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Liu, Xiaoyong…[et al.]. Fuzzy Weighted Least Squares Support Vector Regression with Data Reduction for Nonlinear System Modeling. Mathematical Problems in Engineering No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1208844
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Liu, Xiaoyong& Ouyang, Aijia& Yun, Zhonghua. Fuzzy Weighted Least Squares Support Vector Regression with Data Reduction for Nonlinear System Modeling. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1208844
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1208844
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر