A Hybrid of Functional Networks and Support Vector Machine Models for the Prediction of Petroleum Reservoir Properties
This paper presents an innovative hybrid of Functional Networks and Support Vector Machines (FN-SVM) as an improvement over an existing Functional Networks and Type-2 Fuzzy Logic (FN-T2FL) hybrid model. The former is more promising as it combines two existing techniques that are very close in perfor...
保存先:
| 主要な著者: | , , |
|---|---|
| フォーマット: | Proceeding |
| 言語: | English |
| 出版事項: |
2011
|
| 主題: | |
| オンライン･アクセス: | http://ir.unimas.my/id/eprint/8480/1/Fatai%20Anifowose.pdf http://ir.unimas.my/id/eprint/8480/ http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6122085 |
| タグ: |
タグ追加
タグなし, このレコードへの初めてのタグを付けませんか!
|
| 要約: | This paper presents an innovative hybrid of Functional Networks and Support Vector Machines (FN-SVM) as an improvement over an existing Functional Networks and Type-2 Fuzzy Logic (FN-T2FL) hybrid model. The former is more promising as it combines two existing techniques that are very close in performance and well known for their computational stability and fast processing. This proposed FNSVM hybrid model benefits from the excellent performance of the least-square-based model-selection algorithm of Functional Networks and the non-linear high-dimensional feature transformation capability that is based on structural risk minimization and Tikhonov regularization properties of SVM. Training and testing the SVM component of the hybrid model
with the best and dimensionally-reduced variables from the
input data resulted in better performance with higher
correlation coefficients, lower root mean square errors and
further less execution time than the standard SVM model. A
comparison of FN-SVM with the existing FN-T2FL, using the
same data and operating environment, showed that the FNSVM
is more accurate and consumes less time. |
|---|