In any electrical machine, the loading capacity is limited by its temperature rise. This temperature can be determined by the resistance method. In the present study, the adaptive neuro-fuzzy inference system (ANFIS) is developed to predict the hot resistance of the winding of a switched reluctance motor. This system  is based on  a neuro-fuzzy model, trained with data collected at various operating conditions of the motor. This technique estimates hot resistance of the winding using the input variables as cold resistance, ambient temperature and temperature rise. The predicted results by ANFIS are in good agreement with computed values. The predicted results also proves the supremacy of ANFIS in comparison with other models for the estimation of hot resistance. 

Keywords: Adaptive Neuro Fuzzy Inference system (ANFIS) , hot resistance, temperature rise, Switched Reluctance Machine.