Deep neural network with RBF and sparse auto-encoders for numeral recognition

In this paper we proposed a new deep neural network architecture which is composed from a radial basis function neural network (RBF NN) followed by two auto-encoders and softmax classifier and we presented some comparison between this architecture and other architecture on numeral recognition applications. We gave also a review about RBF and sparse auto-encoder neural networks in the literature. First we defined neural networks and their different type´s especially radial basis function neural networks (RBF NN) due to their specificity. Second we focused on auto-encoders and sparse coding then we moved to sparse auto-encoders and finally we demonstrated the effectiveness of our deep architecture by showing our experimental results and some comparisons.