In this paper an adaptive neuro fuzzy inference system based on interval Gaussian type-2 fuzzy sets in the antecedent part and Gaussian type-1 fuzzy sets as coefficients of linear combination of input variables in the consequent part is presented. The capability of the proposed method (we named ANFIS2) to function approximation and dynamical system identification is shown. The ANFIS2 structure is very similar to ANFIS, but in ANFIS2, a layer has been added for the purpose of type reduction. An adaptive learning rate based backpropagation with convergence guaranteed is used for parameter learning. Finally, the proposed ANFIS2 are used to control of a flexible joint robot arm that can be used in robot arm. Simulation results shows the proposed ANFIS2 with Gaussian type-1 fuzzy set as coefficients of linear combination of input variables in the consequent part has good performance and high accuracy but more training time. In the simulation, ANFIS2 is compared with conventional ANFIS. The results show that, in abrupt changes, the type-2 fuzzy system proof of efficiency and excellence to the type-1 fuzzy system. Simulation results shows the proposed ANFIS2 with Gaussian type-1 fuzzy set as coefficients of linear combination of input variables in the consequent part has good performance and high accuracy but more training time. In the simulation, ANFIS2 is compared with conventional ANFIS. The results show that, in abrupt changes, the type-2 fuzzy system proof of efficiency and excellence to the type-1 fuzzy system.
