Hybrid computational intelligence is defined as a combination of multiple intelligent algorithms such that the resulting model\nhas superior performance to the individual algorithms. Therefore, the importance of fusing two or more intelligent algorithms\nto achieve better performance cannot be overemphasized. In this work, a novel homogenous hybridization scheme is proposed\nfor the improvement of the generalization and predictive ability of support vector machines regression (SVR). The proposed and\ndeveloped hybrid SVR (HSVR) works by considering the initial SVR prediction as a feature extraction process and then employs\nthe SVR output, which is the extracted feature, as its sole descriptor. The developed hybrid model is applied to the prediction of\nreservoir permeability and the predicted permeability is compared to core permeability which is regarded as standard in petroleum\nindustry.The results show that the proposed hybrid scheme (HSVR) performed better than the existing SVR in both generalization\nand prediction ability.The outcome of this research will assist petroleum engineers to effectively predict permeability of carbonate\nreservoirs with higher degree of accuracy and will invariably lead to better reservoir. Furthermore, the encouraging performance\nof this hybrid will serve as impetus for further exploring homogenous hybrid system.
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