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 performance and well known for their computational stability and fast processing. This proposed FN-SVM 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 FN-SVM is more accurate and consumes less time.