A comparative trend in forecasting ability of artificial neural networks and regressive support vector machine methodologies for energy dissipation modeling of off-road vehicles

Energy

Volumn 66, Issue , 2014, Pages 569-576

  Taghavifar, Hamid ^a   Mardani, Aref ^a  

^a URMIA UNIVERSITY   (Iran)

Author keywords

Artificial neural network; Energy loss; Motion resistance; Soil bin; Support vector regression

Indexed keywords

ELASTOPLASTICITY; FORECASTING; MEAN SQUARE ERROR; NEURAL NETWORKS; OFF ROAD VEHICLES; SOILS; WHEELS; COMPLEX NETWORKS; ENERGY DISSIPATION;

ANN (ARTIFICIAL NEURAL NETWORK); COEFFICIENT OF DETERMINATION; MEAN ABSOLUTE PERCENTAGE ERROR; MEAN RELATIVE ERROR; MOTION RESISTANCE; PERFORMANCE INDICES; STATISTICAL CRITERION; SUPPORT VECTOR REGRESSION (SVR); FORECASTING ABILITY;

ENERGY DISSIPATION; OFF ROAD VEHICLES;

ARTIFICIAL NEURAL NETWORK; COMPARATIVE STUDY; DATA SET; DISSIPATION; ELASTICITY; ERROR ANALYSIS; PLASTICITY; REGRESSION ANALYSIS; ELASTOPLASTICITY; ENERGY DISSIPATION; SOIL PROPERTY; STATISTICAL ANALYSIS; TIRE; TRAFFIC EMISSION;

EID: 84896395910     PISSN: 03605442     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.energy.2014.01.022     Document Type: Article

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