Predicting the parameters related to traction of tractor during subsoiling operation using Adaptive Neuro-Fuzzy Inference System (ANFIS)

In this research, the adaptive neuro-fuzzy inference system was used for predicting the parameters related to traction of tractor included of drawbar power (DP), drive wheels slippage (S), traction efficiency (TE) and overall energy efficiency (OEE) in tractor- implement combination under the effect of independent variables included of tine type (subsoiler and paraplow), depth (30, 40 and 50 cm) and forward speed (1.8, 2.3, 2.9 and 3.5 km/h) during subsoiling operation. The field data were used to create the regression and ANFIS models for predicting the studied parameters and the results of them were compared together. The field results showed that all independent variables were effective on the studied parameters except TE. The increment of forward speed and depth resulted in increase of S, DP, OEE and decrease of TE. Moreover, with considering the studied parameters, the paraplow tine was more commodious than subsoiler tine. The results of ANFIS part showed that about S, DP, TE and OEE, the membership functions of Trimf, dsigmf, Primf and Gaussmf with the mean square error of 0.0159, 0.0231, 0.0212 and 0.0224 also correlation coefficient of 0.9996, 0.9999, 0.9985 and 0.9997 caused the best models to predict, respectively. ANFIS models had higher accuracy than regression models and it could be calculated the model outlet for a special inlet using ANFIS outlet surfaces.