Evaluating the effect of tire parameters on required drawbar pull energy model using adaptive neuro-fuzzy inference system

Burt, 1983, Ballast and inflation effect on tyre efficiency, Trans ASAE, 26, 1352, 10.13031/2013.34131 Elwaleed, 2006, Net traction ratio prediction for high-lug agricultural tyre, J Terramechanics, 43, 119, 10.1016/j.jterra.2004.10.002 Shmulevich, 1998, The effect of velocity on rigid wheel performance, J Terramechanics, 35, 189, 10.1016/S0022-4898(98)00022-6 Schreiber, 2008, Influence of soil and tire parameters on traction, Res Agric Eng, 54, 43, 10.17221/3105-RAE Taner, 2012, Prediction of tire tractive performance by using artificial neural networks, Math Comput Appl, 17, 182 Kawase, 2006, An indoor traction measurement system for agricultural tires, J Terramechanics, 43, 317, 10.1016/j.jterra.2005.05.007 Zoz, 2003, 49085 Schreiber, 2007, Comparison of different zero-slip definitions and a proposal to standardize tire traction performance, J Terramechanics, 44, 75, 10.1016/j.jterra.2006.03.003 Pytka, 2006, Effects of reduced inflation pressure and vehicle loading on off-road traction and soil stress and deformation state, J Terramechanics, 43, 469, 10.1016/j.jterra.2005.06.001 Degirmencioglu, 2004, Tractive performance comparisons of radial-ply and bias-ply agricultural tractor drive tires, Agric Eng, 10, 1 Taghavifar, 2013, A hybridized artificial neural network and imperialist competitive algorithm optimization approach for prediction of soil compaction in soil bin facility, Measurement, 46, 2288, 10.1016/j.measurement.2013.04.077 Taghavifar, 2013, Artificial neural network estimation of wheel rolling resistance in clay loam soil, Appl Soft Comput, 13, 3544, 10.1016/j.asoc.2013.03.017 Taghavifar, 2014, Use of artificial neural networks for estimation of agricultural wheel traction force in soil bin, Neural Comput Appl, 24, 1249, 10.1007/s00521-013-1360-8 Taghavifar, 2013, A knowledge-based Mamdani fuzzy logic prediction of the motion resistance coefficient in a soil bin facility for clay loam soil, Neural Comput Appl, 23, 293, 10.1007/s00521-013-1400-4 Taghavifar, 2014, Fuzzy logic system based prediction effort: a case study on the effects of tire parameters on contact area and contact pressure, Appl Soft Comput, 14, 390, 10.1016/j.asoc.2013.10.005 Cross, 2013, Estimating terrain parameters for a rigid wheeled rover using neural networks, J Terramechanics, 50, 165, 10.1016/j.jterra.2013.04.002 Taghavifar, 2014, 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, 66, 569, 10.1016/j.energy.2014.01.022 Taghavifar, 2014, Applying a supervised ANN (artificial neural network) approach to the prognostication of driven wheel energy efficiency indices, Energy, 68, 651, 10.1016/j.energy.2014.01.048 Taghavifar, 2014, Multi-criteria optimization model to investigate the energy waste of off-road vehicles utilizing soil bin facility, Energy, 73, 762, 10.1016/j.energy.2014.06.081 Ullah, 2013, Flood flow modeling in a river system using adaptive neuro-fuzzy inference system, Environ Manag Sustain Dev, 2, 54, 10.5296/emsd.v2i2.3738 Srinivas, 2012, Estimation of subsurface strata of earth using adaptive neuro-fuzzy inference system (ANFIS), Acta Geod Geophys Hung, 47, 78, 10.1556/AGeod.47.2012.1.7 Jang, 1993, ANFIS: adaptive-network-based fuzzy inference system, Syst Man Cybern IEEE Trans, 23, 665, 10.1109/21.256541 Bağdagül, 2012, Predicting optimum cure time of rubber compounds by means of ANFIS, Mater Des, 35, 833, 10.1016/j.matdes.2011.03.062 Taghavifar, 2013, Investigating the effect of velocity, inflation pressure, and vertical load on rolling resistance of a radial ply tire, J Terramechanics, 50, 99, 10.1016/j.jterra.2013.01.005 Šmerda, 2010, Tire inflation and its influence on drawbar characteristics and performance–energetic indicators of a tractor set, J Terramechanics, 47, 395, 10.1016/j.jterra.2010.02.005