A comparison between support vector regression and hybrid approach of adaptive Neuro-fuzzy inference system optimized by imperialist competitive algorithm for the prediction of energy loss of run-off-road vehicles

Hojjat Jafari


The present paper is aimed at modeling the energy loss of run-off-road vehicles using artificial intelligence techniques. Support vector regression (SVR), adaptive Neuro-fuzzy inference system (ANFIS) and adaptive neuro-fuzzy inference system optimized by imperialist competitive algorithm (ANFIS-ICA) methodologies were developed to obtain the best model in terms of several statistical criteria including average absolute relative error (AARE), mean square error (MSE), mean relative error (MRE) and coefficient of determination (R2). A total of 50 data points were derived from experiments at five levels of wheel load (i.e. 0, 0.5, 1, 1.5, and 2 kN), five levels of tire inflation pressure (i.e. 70, 100, 140, 175, and 210 kPa) with two replicates in a capacious soil bin facility utilizing a single wheel-tester. The obtained results revealed that SVR outperformed ANFIS and ANFIS-ICA approaches at the lowest predicting error. These findings shed light on some of the underlying principles of SVR, ANFIS-ICA and ANFIS in prediction of energy loss of run-off-road vehicles. 

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