This paper presents a new type-2 fuzzy based activation function for multilayer feedforward neural networks. Instead of other activation functions, the proposed approach uses a type-2 fuzzy set to accelerate backpropagation learning and reduce number of neurons in the complex net. Furthermore, the type-2 fuzzy based activation function provides to minimize the effects of uncertainties on the neural network. Performance of the type-2 fuzzy activation function is demonstrated by exor and speed estimation of induction motor problems in simulations. The comparison among the proposed activation function and commonly used activation functions shows accelerated convergence and eliminated uncertainties with the proposed method. The simulation results showed that the proposed method is more suitable to complex systems.