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In this study, we propose a novel Interval Type-2 (IT2) Fuzzy activation layer that is composed of Single input IT2 (SIT2) Fuzzy Rectifying Units (FRUs) to improve the learning performances of Deep Neural Networks (DNNs). The novel SIT2-FRU has tunable parameters that not only define the slopes of the positive and negative quadrants but also the characteristic of the input–output mapping of the activation function. The novel SIT2-FRU also alleviates vanishing gradient problem and has a fast convergence rate since it can push the mean activation to around zero by processing the inputs defined in the negative quadrant. Thus, SIT2-FRU gives the opportunity to the DNN to have a better learning behavior as it is capable to express linear or sophisticated input–output mapping by simply tuning the footprint of uncertainty of its IT2 fuzzy sets. In order to examine the performance of the SIT2-FRU, comparative …